JP6513231B2 - Power consumption prediction device, power consumption prediction system, power consumption prediction method, and program - Google Patents

Description

  The present invention relates to a power consumption prediction device, a power consumption prediction system, a power consumption prediction method, and a program.

  In recent years, in order to manage the total power consumption in the demand area, a technology for predicting the power consumption for each electrical device is known. In order to predict the power consumption for each electrical device, it is important to specify the correspondence between the operating state and the power consumption for each electrical device. In order to specify such correspondence, it is necessary to specify power consumption for each electrical device. As a method of specifying power consumption for each electric device, there is a method of providing a current sensor for each electric device.

  However, this method is expensive because it is necessary to prepare a plurality of current sensors. Therefore, various techniques have been proposed to specify the power consumption for each electrical device at low cost. For example, Patent Document 1 discloses a technique for estimating the power consumption of each electric device based on the total power supplied from the power company to the demand place via the master breaker. Specifically, in the technology disclosed in Patent Document 1, the power consumption of the electrical device is estimated based on the change in the operating state of the electrical device and the change in the total power supplied to the demand place.

  Here, the power consumption of the electrical device often depends not only on the operating state of the electrical device, but also on the operating environment of the electrical device. In other words, even if the operating condition of the electric device does not change, the power consumption of the electric device often changes if the operating environment of the electric device changes. The operating environment is, for example, the temperature around the electric device or an elapsed time after operating the electric device.

JP, 2011-254583, A

  However, in the technology disclosed in Patent Document 1, the power consumption of the electrical device is estimated without considering the change in the operating environment of the electrical device. Therefore, in the technology disclosed in Patent Document 1, there is a limit to the accuracy of estimation of the power consumption of the electric device. That is, when the power consumption of the electric device is predicted using the technology disclosed in Patent Document 1, there is a limit to the accuracy of the prediction of the power consumption of the electric device.

  The present invention has been made in view of the above problems, and it is an object of the present invention to provide a power consumption prediction device, a power consumption prediction system, a power consumption prediction method, and a program for predicting power consumption of an electrical device with high accuracy. I assume.

In order to achieve the above object, a power consumption prediction apparatus according to the present invention is:
A current value acquisition unit that acquires a current value of a parameter that indicates an operating environment of a first electric device among the plurality of electric devices and that affects power consumption of the first electric device ;
Obtain first power information on total power consumption of the plurality of electrical devices before the current value changes, and second power information on total power consumption of the plurality of electrical devices after the current value changes Means for acquiring power information;
Correspondence information indicating the correspondence between the value of the parameter and the power consumption of the first electric device based on the first power information and the second power information in response to the change of the current value Generating means for generating
Predicted value acquisition means for acquiring a predicted value of the parameter;
And prediction means for predicting the power consumption of the first electric device based on the correspondence information generated by the generation means and the predicted value acquired by the predicted value acquisition means.

  In the present invention, when the current value of the parameter indicating the operating environment of the first electrical device among the plurality of electrical devices changes, correspondence information indicating the correspondence between the value of the parameter and the power consumption of the first electrical device is generated. Power consumption of the first electrical device is predicted based on the generated correspondence information and the predicted value of the parameter. Therefore, according to the present invention, the power consumption of the electric device can be predicted with high accuracy.

BRIEF DESCRIPTION OF THE DRAWINGS It is a block diagram of the system containing the power consumption prediction apparatus which concerns on Embodiment 1 of this invention. It is a block diagram of the power consumption prediction apparatus which concerns on Embodiment 1 of this invention. It is a figure for demonstrating the function of the power consumption prediction apparatus which concerns on Embodiment 1 of this invention. It is a flowchart which shows the correspondence information generation process which the power consumption prediction apparatus which concerns on Embodiment 1 of this invention performs. It is a flowchart which shows the power consumption prediction process which the power consumption prediction apparatus which concerns on Embodiment 1 of this invention performs. It is a figure which shows the screen which presents the breakdown of power consumption for every unit time. It is a figure which shows the screen which alert | reports that peak power has exceeded the threshold value. It is a figure which shows the screen which presents the breakdown of the power consumption per day. It is a figure for demonstrating the function of the power consumption prediction apparatus which concerns on Embodiment 2 of this invention. It is a figure which shows the power consumption characteristic for every classification of an electric equipment. It is a flowchart which shows the apparatus utilization evaluation process which the power consumption prediction apparatus which concerns on Embodiment 2 of this invention performs. It is a flowchart which shows the power consumption prediction process which the power consumption prediction apparatus which concerns on Embodiment 2 of this invention performs. It is a figure which shows the daily schedule of the user in a demand place. It is a figure which shows the weekly schedule of the user in a demand place. It is a figure which shows the screen which presents the power consumption for every classification of an electric equipment. It is a figure for demonstrating the function of the power consumption prediction apparatus which concerns on Embodiment 3 of this invention. It is a flowchart which shows the correspondence information generation process which the power consumption prediction apparatus which concerns on Embodiment 3 of this invention performs. It is a 1st figure for demonstrating the principle of the operation for identification of the power consumption characteristic which the power consumption prediction apparatus which concerns on Embodiment 4 of this invention performs. It is a 2nd figure for demonstrating the principle of the operation for identification of the power consumption characteristic which the power consumption prediction apparatus which concerns on Embodiment 4 of this invention performs. It is a 3rd figure for demonstrating the principle of the operation for identification of the power consumption characteristic which the power consumption prediction apparatus which concerns on Embodiment 4 of this invention performs.

(Embodiment 1)
First, with reference to FIG. 1, a system including a power consumption prediction apparatus 100 according to the first embodiment of the present invention will be described. The power consumption prediction apparatus 100 is an apparatus that predicts various types of power consumption at a demand place. Specifically, the power consumption prediction apparatus 100 is, for example, a total power consumption value which is a total value of power to be consumed in the future at a demand location, and a total power consumption which is a total value of power consumption in the future at a demand location. The future power consumption of each electrical device that consumes power at the demand point, and the future power consumption of each electrical device that consumes power at the demand point are predicted.

  For example, the power consumption prediction apparatus 100 estimates the power consumption characteristics of each electrical device (procedure 1), and predicts the power consumption of each electrical device based on the estimated power consumption characteristics of each electrical device (procedure 2), (Procedure 3) The total power consumption value at the demand place is predicted by calculating the total sum of the predicted power consumption of each electric device, and (Procedure 4) the total power consumption at the demand place is integrated by integrating the predicted total power consumption value Predict. The power consumption prediction apparatus 100 estimates the power consumption characteristics of each electrical device (procedure 1), and estimates the current power consumption of each electrical device based on the estimated power consumption characteristics of each electrical device (procedure 5) (Procedure 6) It is also possible to estimate the power consumption of each electric device by integrating the estimated current power consumption of each electric device. In (Procedure 5), the breakdown of the current power consumption of each electrical device with respect to the current total power consumption value is estimated. In (Procedure 6), the breakdown of the current power consumption of each electrical device with respect to the current total power consumption is estimated.

  The power consumption prediction apparatus 100 may estimate the power consumption characteristics of a combination of a plurality of electrical devices instead of estimating the power consumption characteristics for each electrical device in (Procedure 1). Estimating the power consumption characteristics of the electrical device basically generates correspondence information indicating the correspondence between the power consumption of the electrical device and the value of the parameter indicating the operating state of the electrical device and the operating environment of the electrical device. It is. The operating state is defined by an operating mode, a target value, and the like. For example, when the electric device is an air conditioner, the operation state is defined by an operation mode (heating operation, cooling operation, blowing operation, dehumidifying operation, etc.) and a set temperature.

  The operating environment is defined by the environment in which the electrical device is placed. The parameters indicating the operating environment are, for example, temperature, humidity, weather, season, date, day of the week, time zone, location of demand place, number of family members, and schedule of the user. In this embodiment, in order to facilitate understanding, the elapsed time from the time when the operation of the electric device is changed is also a parameter indicating the operating environment. The power consumption of the electric device may be dependent on the operating environment or may not depend on the operating environment. For example, when the electric device is an air conditioner, the operating state is a cooling operation state, and the operating environment is air temperature or the elapsed time described above, the power consumption of the electric device is likely to depend on the operating environment. On the other hand, even if the electric device is an air conditioner, if the operating state is the air blowing operation state, the power consumption of the electric device may not depend on the operating environment, even if the operating environment is air temperature or the elapsed time described above. There is. In addition, when the electrical device is lighting and the operating environment is air temperature or the elapsed time described above, the power consumption of the electrical device is likely to be independent of the operating environment. In the present embodiment, the power consumption prediction apparatus 100 is capable of determining what kind of operating environment affects the power consumption when what kind of electric device is in what kind of operation state. .

  In the present embodiment, the correspondence information is not information indicating the correspondence between the power consumption and the operation state, but information indicating the correspondence between the power consumption, the operation state, and the value of the parameter indicating the operation environment. Therefore, in the present embodiment, since the power consumption characteristic is estimated in consideration of not only the operating state but also the parameter value indicating the operating environment, it can be expected that the accuracy of the prediction of the power consumption will be high. Note that generating or updating the correspondence information can also be said to learn power consumption characteristics. In addition, there may be a case where only one of the on state and the off state can be taken as an operation state of the electric device. For example, in the case of an illumination that does not have a light control function, the electric device can be in either an on state or an off state. In such a case, the correspondence relationship information may be information indicating the correspondence relationship between the power consumption and the value of the parameter indicating the operating environment.

  In the present embodiment, the power consumption prediction apparatus 100 is not only the power consumption prediction but also a device that manages the power consumption, that is, a power management device. Further, in the present embodiment, the power consumption prediction apparatus 100 has a function of controlling and monitoring an electric device operating at a demand place. Specifically, in the present embodiment, the power consumption prediction apparatus 100 has a function as a HEMS controller in a home energy management system (HEMS).

  The power consumption prediction apparatus 100 communicates with the power measurement apparatus 200, the sensor 210, the electric device 301, the electric device 302, the electric device 303, and the power conversion device 410 via the in-home network 610. The power consumption prediction apparatus 100 also communicates with the terminal device 510 and the cloud server 520 via the in-home network 610 and the out-of-home network 620. The power consumption prediction apparatus 100 can control and monitor the electric device 301, the electric device 302, and the electric device 303. That is, the electric device 301, the electric device 302, and the electric device 303 are management target devices. Since the power consumption prediction apparatus 100 grasps the operation state of the management target device, the power consumption prediction device 100 can generate correspondence relationship information of the management target device.

  On the other hand, the power consumption prediction apparatus 100 can not control and monitor the electric device 304 and the electric device 305. That is, the electric device 304 and the electric device 305 are non-managed devices. Since the power consumption prediction apparatus 100 basically does not know the operation state of the non-managed device, it can not necessarily generate correspondence relationship information of the non-managed device. However, the power consumption prediction apparatus 100 can grasp the operation state of the non-managed device and generate correspondence relationship information of the non-managed device, for example, by referring to the schedule information of the non-managed device. The configuration of the power consumption prediction apparatus 100 will be described below with reference to FIG.

  As illustrated in FIG. 2, the power consumption prediction apparatus 100 includes a central processing unit (CPU) 11, a read only memory (ROM) 12, a random access memory (RAM) 13, a flash memory 14, a real time clock (RTC) 15, A touch screen 16, an in-home communication interface 17, and an out-of-home communication interface 18 are provided. The components included in the power consumption prediction apparatus 100 are mutually connected via a bus.

  The CPU 11 controls the overall operation of the power consumption prediction apparatus 100. The CPU 11 operates according to a program stored in the ROM 12 and uses the RAM 13 as a work area. The ROM 12 stores programs and data for controlling the overall operation of the power consumption prediction apparatus 100. The RAM 13 functions as a work area of the CPU 11. That is, the CPU 11 temporarily writes a program or data to the RAM 13 and refers to the program or data as appropriate.

  The flash memory 14 is a non-volatile memory that stores various types of information. The RTC 15 is a device for clocking. The RTC 15 has a built-in battery, for example, and continues clocking even while the power consumption prediction apparatus 100 is powered off. The RTC 15 includes, for example, an oscillation circuit including a quartz oscillator.

  The touch screen 16 detects a touch operation made by the user and supplies a signal indicating the result of the detection to the CPU 11. The touch screen 16 also displays an image based on the image signal supplied from the CPU 11. Thus, the touch screen 16 functions as a user interface of the power consumption prediction apparatus 100.

  The in-home communication interface 17 is an interface for connecting the power consumption prediction apparatus 100 to the in-home network 610. The power consumption prediction apparatus 100 can communicate with various devices connected to the home network 610 via the home network 610. The in-home communication interface 17 includes, for example, a LAN (Local Area Network) interface (for example, a NIC (Network Interface Card)).

  The out-of-home communication interface 18 is an interface for connecting the power consumption prediction apparatus 100 to the out-of-home network 620. The power consumption prediction apparatus 100 can communicate with various devices connected to the outside network 620 via the outside network 620. The external communication interface 18 includes, for example, a LAN interface (for example, a NIC).

  The power measuring device 200 measures the total power (hereinafter referred to as “total power consumption”) supplied from the commercial power supply 400 to each electrical device in the demand area via a master breaker (not shown). The power measuring apparatus 200 measures the value of the current flowing through the power line and the value of the voltage between the power lines at a main breaker portion (not shown). The power measuring apparatus 200 calculates the total power consumption in a unit period, the cumulative total power consumption from a reference time, and the like based on the measured total power consumption. The power measurement apparatus 200 stores, as power information, information indicating physical quantities acquired by measurement or calculation. The power measurement apparatus 200 transmits the stored power information to the power consumption prediction apparatus 100 via the in-home network 610 in a predetermined cycle (for example, every 30 minutes). The power measurement device 200 includes a current detection sensor, a voltage detection sensor, a flash memory, a communication interface, and the like.

  The sensor 210 detects the value of the parameter indicating the operating environment, and transmits the value of the detected parameter to the power consumption prediction apparatus 100 via the home network 610. The sensor 210 is, for example, a temperature sensor or a humidity sensor.

  The electrical devices 301, 302, 303, 304, and 305 are devices disposed in the house and are devices that consume and operate from the electrical energy supplied from the commercial power supply 400. The electric devices 301, 302, and 303 have the same configuration as the in-home communication interface 17, and have a function of connecting to the in-home network 610. On the other hand, the electric devices 304 and 305 do not have the same configuration as the in-home communication interface 17 and do not have the function of connecting to the in-home network 610. That is, the electric devices 301, 302, and 303 are management target devices, and the electric devices 304 and 305 are non-management devices.

  The electric devices 301, 302, 303, 304, 305 are, for example, air conditioners, televisions, lights, refrigerators, IHCH (Induction Heating Cooking Heater), eco cute, ventilation fans, ice making machines, lights, insecticides, showcases, dehumidifiers, humidifications. Appliances, heating equipment, electric kettles, microwave ovens, rice cookers, bath dryers, etc.

  The commercial power supply 400 is a power supply for the power company to supply power to customers. The power supplied by the commercial power source 400 is AC power. The commercial power supply 400 supplies AC power to the demand place. In the present embodiment, in order to facilitate understanding, it is assumed that power is not supplied from the demand place to the commercial power supply 400 (that is, there is no reverse power flow).

  The power conversion device 410 converts AC power into DC power and converts DC power into AC power. The power conversion device 410 converts AC power supplied from the commercial power supply 400 into DC power and supplies the DC power to the electric vehicle 420 (strictly speaking, a storage battery mounted on the electric vehicle 420). In addition, the power conversion device 410 converts DC power supplied from the electric vehicle 420 into AC power and supplies the AC power to the electric devices 301, 302, 303, 304, and 305. The power conversion apparatus 410 has a function of communicating with the power consumption prediction apparatus 100 via the in-home network 610.

  Electric vehicle 420 includes a storage battery (not shown). The storage battery stores the power supplied from the power conversion device 410. The electric vehicle 420 can travel with the power stored in the storage battery. Further, the storage battery supplies the stored power to the power conversion device 410. Note that a combination of the power conversion device 410 and the power conversion device 410 can also be regarded as an electrical device.

  The terminal device 510 is a device that communicates with the power consumption prediction apparatus 100 via the outside network 620. The terminal device 510 functions as, for example, a user interface of the power consumption prediction apparatus 100. The terminal device 510 is, for example, a personal computer, a smartphone, a mobile phone, a tablet terminal, or the like.

  The cloud server 520 is a server that provides resources in cloud computing. The cloud server 520 includes, for example, a processor 521 that controls the overall operation of the cloud server 520, a communication interface 522 for connecting to the outside network 620, and a hard disk 523 that stores various types of information. The cloud server 520 supplies the information stored in the hard disk 523 to the power consumption prediction apparatus 100 in response to a request from the power consumption prediction apparatus 100. In addition, in response to a request from the power consumption prediction apparatus 100, the cloud server 520 executes the requested processing, and transmits information indicating the processing result to the power consumption prediction apparatus 100.

  The home network 610 is a network established in the home and is a network for devices connected to the home network 610 to communicate with each other. The home network 610 is, for example, a wireless LAN.

  The out-of-home network 620 is a network established outside the home, and is a network through which devices connected to the out-of-home network 620 communicate with each other. The outside network 620 is, for example, a WAN (Wide Area Network). The home network 610 and the outside network 620 are connected to each other via the power consumption prediction apparatus 100. Therefore, the power consumption prediction apparatus 100 also has a function as a gateway apparatus. The outside network 620 is, for example, the Internet.

  Next, basic functions of the power consumption prediction apparatus 100 will be described with reference to FIG. The power consumption prediction apparatus 100 functionally includes a power information acquisition unit 101, a current value acquisition unit 1021, a predicted value acquisition unit 1022, a storage unit 103, a generation unit 104, a prediction unit 105, a control unit 106, and a presentation unit 107. Prepare.

  The power information acquisition unit 101 acquires power information on the total power consumption supplied from the commercial power supply 400 to the electrical devices 301, 302, 303, 304, and 305. The power information acquisition unit 101 acquires, for example, power information related to the power supplied from the commercial power supply 400 to the demand point via a master breaker (not shown). In the present embodiment, the power information acquisition unit 101 periodically acquires power information. Therefore, the power information acquisition unit 101 acquires the first power information and the second power information. The first power information is information on the total power consumption before the current value of the parameter described later changes. The second power information is information on the total power consumption after the current value of a parameter described later changes. The function of the power information acquisition unit 101 is realized by, for example, the function of the home communication interface 17.

  The current value acquisition unit 1021 acquires the current value of the parameter indicating the operation environment of the first electrical device among the electrical devices 301, 302, 303, 304, and 305. The first electrical device is, for example, an electrical device in operation. That is, the current value acquisition unit 1021 acquires the value of the parameter indicating the operating environment for the operating electric device among the electric devices 301, 302, 303, 304, and 305. The current value acquisition unit 1021 acquires, for example, the value of a parameter that affects the power consumption of the operating electric device from the sensor 210 or the cloud server 520. The function of the current value acquisition unit 1021 is implemented by, for example, the functions of the in-home communication interface 17 and the out-of-home communication interface 18. Alternatively, the function of the current value acquisition unit 1021 is realized by the function of the RTC 15, for example.

  The predicted value acquisition unit 1022 acquires the predicted value of the above-described parameter. The predicted value acquisition unit 1022 acquires, for example, a predicted value of a parameter that affects the power consumption of the first electric device from the sensor 210 or the cloud server 520. The function of the predicted value acquisition unit 1022 is realized, for example, by the functions of the in-home communication interface 17 and the out-of-home communication interface 18. Alternatively, the function of the predicted value acquisition unit 1022 is realized by the function of the RTC 15, for example.

  The storage unit 103 functionally includes a power information storage unit 1031, a parameter value storage unit 1032, a correspondence relationship information storage unit 1033, an operation state information storage unit 1034, and a schedule information storage unit 1036. The power information storage unit 1031 stores the power information acquired by the power information acquisition unit 101 in association with the acquisition time. The parameter value storage unit 1032 stores the current value of the parameter acquired by the current value acquisition unit 1021 in association with the acquisition time. When the parameter acquired by the current value acquisition unit 1021 is a parameter that does not change with the passage of time, the current value of the acquired parameter may not be associated with the acquisition time. The parameter value storage unit 1032 stores the predicted value of the parameter acquired by the predicted value acquisition unit 1022 in association with the time to be predicted. That is, the parameter value storage unit 1032 stores information in which the value of the parameter is associated with the time.

  The correspondence relationship information storage unit 1033 stores the correspondence relationship information generated by the generation unit 104 in association with the acquisition time. In addition, it can be said that the power consumption characteristic indicated by these correspondence information is likely to be likely (that is, the accuracy of the power consumption characteristic is high) that a large number of correspondence information indicating the same or similar power consumption characteristics is acquired. . In this case, it can be said that the accuracy of the correspondence information is high. Further, it can be said that the correspondence information with a newer acquisition time is more accurate. When many pieces of correspondence information indicating the same or similar power consumption characteristics are acquired, these correspondence information may be integrated to give high accuracy. The correspondence relationship information storage unit 1033 can perform the removal of outliers, averaging, adoption of a median, and the like on the plurality of pieces of correspondence relationship information acquired, and can improve the accuracy of the correspondence relationship information.

  The operation state information storage unit 1034 stores the operation state information generated by the control unit 106 in association with the acquisition time.

  The schedule information storage unit 1036 stores schedule information including the use of the electric device by the user and the leaving schedule. For example, breakfast from 7:00 to 8:00, shopping from 10:00 to 11:00, setting from 11:30 to 12:00 for cooking, with or without equipment used for each schedule, electric car The usage schedule of 420 is united and stored in the schedule information storage unit 1036. The schedules of a plurality of users may be managed in a single schedule. For example, all families who are consumers of power at the consumers may be combined into one schedule. The functions of the storage unit 103, the power information storage unit 1031, the parameter value storage unit 1032, the correspondence information storage unit 1033, the operation state information storage unit 1034, and the schedule information storage unit 1036 are realized by the function of the flash memory 14, for example. .

  When the value of the parameter that affects the power consumption of the operating electric device changes, the generation unit 104 operates based on the power information acquired by the power information acquisition unit 101 before and after the value of this parameter changes. Generate correspondence information on electrical devices in That is, when the current value of the above-described parameter changes, the generation unit 104 generates correspondence relationship information indicating the correspondence relationship between the value of the above-described parameter and the power consumption of the first electric device. The correspondence relationship information is information indicating the correspondence relationship between the operating state of the operating electric device, the value of this parameter, and the power consumption of the operating electric device. For example, it is assumed that the parameter that affects the power consumption of the operating electric device is temperature. In this case, the difference between the total power consumption obtained before the temperature change and the total power consumption obtained after the temperature change is in operation as it is due to the change in power consumption of the operating electric device. Generate correspondence information of electrical devices of

  For example, it is assumed that the electric device in operation is an air conditioner, and the air conditioner is cooling at a set temperature of 20 ° C. Here, the total power consumption is 3000 W when the ambient temperature is 30 ° C., and the total power consumption is 2800 W when the ambient temperature is 28 ° C. In this case, it can be considered that the power consumption of the air conditioner is reduced by 200 W due to the decrease of the ambient temperature by 2 ° C. Here, temporarily, according to the specifications of the air conditioner and the identification operation of the air conditioner, it is known that the ambient temperature is 30 ° C. and the power consumption of the air conditioner is 600 W when the air conditioner performs the cooling operation at the set temperature 20 ° C. It shall be. That is, temporarily, correspondence relationship information is stored in advance, which correlates that the air conditioner is cooling at a set temperature of 20 ° C., that the ambient temperature is 30 ° C., and that the power consumption of the air conditioner is 600 W. It is assumed that In this case, it can be seen that the ambient temperature is 28 ° C., and the power consumption of the air conditioner is 400 W when the air conditioner is cooling at a set temperature of 30 ° C. In other words, correspondence information is newly generated that correlates the cooling operation of the air conditioner at the set temperature of 20 ° C., the ambient temperature of 28 ° C., and the power consumption of the air conditioner of 400 W.

  Also, if the parameter indicating the operating environment is an elapsed time from the time of changing the operation of the electric device, strictly speaking, the parameter always changes. However, it is not realistic for the generation unit 104 to always generate correspondence information. Therefore, when the parameter is the elapsed time, the generation unit 104 generates correspondence relationship information, for example, each time a predetermined time has elapsed. That is, when the elapsed time has changed by the threshold time or more, the generation unit 104 can consider that the parameter has changed. The same applies to the case where the parameter is not the elapsed time described above. That is, when the parameter has changed by the threshold or more, the generation unit 104 can consider that the parameter has changed.

  In addition, the generation unit 104 may generate correspondence relationship information for a combination of a plurality of electrical devices. That is, the correspondence information is not limited to the information indicating the power consumption characteristics of one electric device, and may be information indicating the total power consumption characteristics of a plurality of electric devices. For example, in the case where there are a plurality of operating electric devices, the correspondence relationship information indicates the operating states of the plurality of electric devices, the parameters indicating the operating environments of the plurality of electric devices, and the plurality of electric devices. The information may correspond to the total value of the power consumption of the device. In this case, the combination of a plurality of electric devices can also be regarded as the first electric device described above. The function of the generation unit 104 is realized, for example, by cooperation of the CPU 11 and the RAM 13.

  The prediction unit 105 predicts the power consumption of the electrical device in which the correspondence information is generated, based on the correspondence information generated by the generation unit 104 and the predicted value acquired by the predicted value acquisition unit 1022. For example, with regard to an electrical device for which correspondence relationship information has been generated, the prediction unit 105 predicts a parameter related to the power consumption of the electrical device, assuming that the operation state of the electrical device is grasped. Power consumption can be predicted from the value. The prediction unit 105 may predict the power consumption by calculation, or may predict the power consumption with reference to a table. The function of the prediction unit 105 is realized, for example, by cooperation of the CPU 11 and the RAM 13.

  Here, the prediction unit 105 can estimate not only the power consumption of the first electric device in the future but also the power consumption of the first electric device at the present time. In this case, the prediction unit 105 estimates the power consumption of the first electric device at the current time based on the correspondence relationship information generated by the generation unit 104 and the current value of the parameter acquired by the current value acquisition unit 1021. In this case, the prediction unit 105 can estimate the current ratio of the power consumption of the first electrical device to the total power consumption at the demand place.

  Here, when the operating state of any one of the plurality of electric devices changes, the prediction unit 105 can predict the power consumption of the electric device whose operating state has changed. For example, when the operation state of the electrical device for which the correspondence information is generated changes, the prediction unit 105 can predict the power consumption of the electrical device for which the correspondence information is generated. That is, the prediction unit 105 does not periodically predict the power consumption of the electrical device, but performs less processing by predicting the power consumption of the electrical device when it is clear that the power consumption of the electrical device changes. Predict power consumption by load. The prediction unit 105 may predict the power consumption by adopting the initial value of the power consumption acquired from the user or the cloud server 520 when the accuracy of the correspondence information is equal to or less than the specified value.

  In addition, the prediction unit 105 may further predict total power consumption or total power consumption of the plurality of electrical devices by, for example, predicting power consumption or power consumption of the electrical device for which the correspondence information is generated. it can. For example, when correspondence information is generated for all the electric devices, the prediction unit 105 predicts the power consumption or the power consumption of each electric device to calculate the total power consumption or the total power consumption of all the electric devices. Can be predicted.

  The prediction unit 105 stores the power stored in the power information storage unit 1031 for at least one of the management target device in operation and the management target device and the non-management device predicted to be used by the schedule information. Based on the information and the predicted value of the parameter stored in the parameter value storage unit 1032, at least one of the power consumption and the power consumption is predicted.

  The control unit 106 controls the electric devices 301, 302, and 303 by transmitting control signals to the electric devices 301, 302, and 303. Further, the control unit 106 can grasp the operation state of the electric devices 301, 302, and 303 by receiving a signal indicating the operation state from the electric devices 301, 302, and 303. The function of the control unit 106 is realized, for example, by the CPU 11 and the in-home communication interface 17 cooperating with each other.

  When the total power consumption or the total power consumption predicted by the prediction unit 105 exceeds the threshold value, the control unit 106 controls the power consumption target electrical equipment such that the power consumption of the power consumption target electrical equipment is reduced. The control unit 106 controls the power saving target electric device after causing the presenting unit 107 to present the effect of executing the power saving control, the information indicating the electric device targeted for the power saving control, or the effect obtained by the power saving. May be

  If the total power consumption or the total power consumption predicted by the prediction unit 105 exceeds a predetermined threshold, the presentation unit 107 presents information indicating an electrical device to be saved among the plurality of electrical devices. The threshold is, for example, a threshold of power or a threshold of power in a fixed period, and is a contract power or a target power or target power set by a user. The fixed period is, for example, one day. The electrical devices to be saved are, for example, electrical devices with a large reduction in power consumption due to power saving. For example, the breakdown of the total power consumption or the breakdown of the total power consumption is determined with reference to the electrical devices to be saved.

  Here, the power saving target electric devices do not have to select one having a large power saving effect. For example, reference values for operating time and power consumption may be set, and an electric device that has been controlled to exceed the reference value in a unit period may be selected as a power saving target. The length of the unit period is, for example, one hour or one day. The reference value of the operation time or the power consumption may be an average value of the operation time or the power consumption. The function of the presentation unit 107 is realized, for example, by cooperation of the CPU 11 and the touch screen 16. Alternatively, the function of the presentation unit 107 is realized by the cooperation of the CPU 11 and the in-home communication interface 17. In this case, the presentation unit 107 presents information to the user via the terminal device 510, for example.

  The presentation unit 107 presents the total power consumption or the total power consumption predicted by the prediction unit 105 to the user. The presentation unit 107 may present to the user the power consumption or the amount of power consumption of each electric device predicted by the prediction unit 105, that is, the breakdown of the power consumption or the amount of power consumption. FIG. 6 shows an example in which the breakdown of power consumption for each predicted time zone is presented. FIG. 8 shows an example of presenting the breakdown of the estimated daily power consumption. Note that the presentation unit 107 may present a breakdown of the current total power consumption or the total power consumption. That is, the presentation unit 107 may present the ratio of the power consumption or the power consumption of each electrical device estimated by the prediction unit 105 to the current total power consumption or the total power consumption.

  Also, when the total power consumption predicted by the prediction unit 105 exceeds the upper limit value, the presentation unit 107 presents to the user that the predicted total power consumption exceeds the upper limit value (2500 W), as shown in FIG. 7. Do. FIG. 7 shows an example in which the transition of power consumption is predicted at 04:00, and the predicted value of power consumption by the operating electric devices including the IHCH operated at 04:00 exceeds the upper limit at 06:00. It shows.

  Next, the correspondence relationship information generation processing performed by the power consumption prediction apparatus 100 will be described with reference to FIG. The correspondence relationship information generation process is repeatedly performed, for example, in a fixed cycle. The fixed cycle is, for example, 10 minutes or 1 hour.

  First, the CPU 11 acquires power information (step S101). The CPU 11 acquires power information from the power measurement device 200 via, for example, the in-home communication interface 17. The CPU 11 stores the acquired power information in the flash memory 14 (power information storage unit 1031).

  When the process of step S101 is completed, the CPU 11 acquires operation state information (step S102). For example, the CPU 11 acquires operation state information from the management target device via the in-home communication interface 17. The CPU 11 stores the acquired operation state information in the flash memory 14 (operation state information storage unit 1034).

  CPU11 will acquire the present value of a parameter, if the processing of Step S102 is completed (Step S103). The CPU 11 acquires the current value of the parameter from the sensor 210 via, for example, the in-home communication interface 17. The CPU 11 stores the current value of the acquired parameter in the flash memory 14 (parameter value storage unit 1032).

  CPU11 will calculate consumption destination unknown power, if the process of step S103 is completed (step S104). The power consumption unknown power is power whose power consumption is unknown. The consumption destination unknown power is obtained by subtracting the value of the power consumption of the management target device whose power consumption is clear from the value of the total power consumption indicated by the power information acquired in step S101. The management target device whose power consumption is clear is a device that can measure power consumption by itself and can transmit the measured power consumption to the power consumption prediction apparatus.

  When the process of step S104 is completed, the CPU 11 determines whether there is an electrical device whose operating condition has changed (step S105). The electric device whose operating condition has changed is an electric device in which at least one of the operating state and the current value of the parameter affecting the power consumption has changed. If it is determined that there is an electrical device whose operating condition has changed (step S105: YES), the CPU 11 determines whether there is one electrical device whose operating condition has changed (step S106).

  When the CPU 11 determines that there is only one electric device whose operating condition has changed (step S106: YES), the consumption destination unknown power calculated at step S104 last time and the consumption destination calculated at step S104 this time Correspondence information of the electric device whose operating condition has changed is generated by correlating the difference between the unknown power and the change of the operating condition.

  When it is determined that the number of electric devices whose operating conditions have changed is not one (step S106: NO), the CPU 11 corrects the power consumption unknown power (step S108). Specifically, the CPU 11 estimates the total value of the fluctuation values of the power consumption of the electric device with the lowest accuracy among the electric devices whose operating conditions have changed. Then, a new consumption destination unknown is a value obtained by subtracting the estimated total value from the difference between the consumption unknown power calculated in step S104 last time and the consumption destination unknown power calculated in step S104 this time. Calculated as fluctuation of power.

  When the process of step S108 is completed, the CPU 11 assumes that the fluctuation of the consumption-unknown power obtained in step S108 is the fluctuation of the power consumption of the electric device having the lowest accuracy, and the accuracy is the lowest. The correspondence relationship information of the electric device is generated (step S109). The CPU 11 can appropriately execute removal of outliers, averaging, and adoption of a median with respect to a plurality of pieces of correspondence information generated under the same condition or similar conditions.

  If the CPU 11 determines that there is no electrical device whose operating condition has changed (NO in step S105), the total power consumption indicated by the power information acquired in step S101 last time and the power acquired in step S101 this time The correspondence relationship information of the non-management device is generated as the difference between the total power consumption indicated by the information and the variation of the power consumption by the non-management device. For example, when the washing is set from 8 o'clock to 9 o'clock according to the schedule information, it is assumed that the power consumption in this period is the washing machine, and the correspondence relation information of the washing machine is generated. When the CPU 11 completes the processing of step S107, step S109, and step S110, it completes the correspondence relationship information generation processing.

  Next, the power consumption prediction process performed by the power consumption prediction apparatus 100 will be described with reference to FIG. The power consumption prediction process is repeatedly performed, for example, in a fixed cycle. The fixed cycle is, for example, one minute or five minutes. Alternatively, the power consumption prediction process may be performed in response to a change in the operating state of any of the electrical devices.

  First, the CPU 11 acquires power information as in step S101 (step S201). When the process of step S201 is completed, the CPU 11 acquires operation state information as in step S102 (step S202). CPU11 will acquire the predicted value of a parameter, if processing of Step S202 is completed (Step S203).

  When the process of step S203 is completed, the CPU 11 predicts the power consumption or the power consumption of the device to be managed (step S204). Specifically, the CPU 11 stores correspondence relationship information stored in the correspondence relationship information storage unit 1033, predicted values of parameters stored in the parameter value storage unit 1032, and operation status information stored in the operation status information storage unit 1034. Based on the above, at least one of the power consumption by the managed device in operation and the power consumption by the managed device in operation within a predetermined period or until a predetermined time is predicted. The fixed period is, for example, one hour or one day. The fixed time is, for example, 24 o'clock. Note that among devices to be managed that can directly acquire power consumption among the devices to be managed, power consumption acquired without predicting power consumption is adopted.

  When the process of step S204 is completed, the CPU 11 predicts the power consumption or the power consumption of the non-managed device in the same procedure as step S204 (step S205). When the process of step S205 is completed, the CPU 11 determines whether the predicted power consumption or the power consumption is equal to or more than a threshold (step S206). The predicted power consumption is the sum of the power consumption of the management target device predicted in step S204 and the power consumption of the non-management device predicted in step S205. Further, the predicted power consumption is the sum of the power consumption of the management target device predicted in step S204 and the power consumption of the non-management device predicted in step S205.

  If the CPU 11 determines that the predicted power consumption or the power consumption is equal to or more than the threshold (YES in step S206), the management target device is selected based on the correspondence information stored in the correspondence information storage unit 1033. The difference between the power consumption in the case where power saving is not performed and the power consumption in the case where power saving is performed is estimated, and the estimated difference is set as the power consumption reduction room. The power saving operation is realized by setting the operation mode of the management target device to the power saving mode and setting the management target device to an operation state with low power consumption. Although the power consumption in the power saving operation is described as being predicted based on the correspondence information, it is calculated based on the power value acquired from the user or the cloud server 520 or the parameter acquired from the user or the cloud server 520 It may be a power value to be calculated. For example, the power consumption in the power saving operation may be calculated to be 80% of the power consumption in the normal operation.

  When the process of step S207 is completed, the CPU 11 sequentially selects, as power saving targets, the management target devices with the largest power saving capacity until the predicted power consumption or the power consumption becomes equal to or less than the threshold (step S208).

  In step S208, an example in which the CPU 11 selects a power saving device has been described. In step S208, the CPU 11 may present the predicted value of the power consumption and the power saving capacity to the user via the touch screen 16 for each management target device, and allow the user to select the power saving device. Further, in step S208, the CPU 11 may present the predicted value of the power consumption and the power saving capacity to the user via the touch screen 16 for each non-management device, and urge the user to save power for the non-management device.

  Then, when the process of step S208 is completed, the CPU 11 executes power saving on the management target device selected in step S207 (step S209). If the CPU 11 determines that the predicted power consumption or the power consumption is not equal to or more than the threshold (step S206: NO), or if the process of step S209 is completed, the power consumption prediction process is completed.

  In the present embodiment, an example has been described in which the power consumption prediction apparatus 100 is an HEMS controller, and the power consumption prediction apparatus 100 is directly connected to a device to be managed. In the present invention, the power consumption prediction apparatus 100 may be indirectly connected to the management target device via the HEMS controller. In this case, the HEMS controller assigns an IP address to the management target device, and relays communication between the power consumption prediction apparatus 100 and the management target device.

  In the present embodiment, when the current value of the parameter indicating the operating environment of the first electric device among the plurality of electric devices changes, the correspondence information indicating the correspondence between the value of the parameter and the power consumption of the first electric device is The power consumption of the first electrical device is predicted based on the generated correspondence information and the predicted value of the parameter. Therefore, according to the present embodiment, the power consumption of the electric device can be predicted with high accuracy. Further, in the present embodiment, the power consumption characteristic in which the change in the power consumption accompanying the change in the operating environment is taken is efficiently obtained, triggered by the change in the operating environment. For this reason, according to the present embodiment, the processing load for acquiring the power consumption characteristic is reduced.

  Further, in the present embodiment, under the situation where it is not possible to measure the individual power consumption of the electric device which is the management target device or the non-management device, the time axis fluctuation of the power consumption accompanying the change of the operating environment affecting the power consumption The power consumption characteristics of the device are learned. For this reason, according to the present embodiment, the breakdown of the power consumption results and the prediction of the power consumption transition are more accurate than when learning the power consumption characteristics of the electric device only from the change in the operating state of the electric device. It becomes feasible.

  Further, in the present embodiment, the transition of power consumption is predicted in response to a change in the operating state of the electric device. Therefore, according to the present embodiment, it is possible to predict that the total power consumption or the total power consumption will exceed the threshold along with the operation of the electric device in operation. For this reason, in the present embodiment, it is possible to prevent the total power consumption or the total power consumption from exceeding the threshold.

  Further, in the present embodiment, when the predicted total power consumption or the total power consumption amount exceeds the threshold, the power saving target electrical device is controlled such that the power consumption of the power saving target electrical device is reduced. For this reason, in the present embodiment, it is possible to prevent the total power consumption or the total power consumption from exceeding the threshold.

Second Embodiment
In the first embodiment, basically, an example in which the power consumption and the power consumption amount are managed for each electric device has been described. In the second embodiment, an example of managing power consumption and power consumption for each type of electric device will be described. Hereinafter, differences from the first embodiment will be mainly described.

  First, basic functions of the power consumption prediction apparatus 120 according to the present embodiment will be described with reference to FIG. The power consumption prediction apparatus 120 functionally includes a power information acquisition unit 101, a current value acquisition unit 1021, a predicted value acquisition unit 1022, a storage unit 103, a generation unit 104, a prediction unit 105, a control unit 106, and a presentation unit 107. Prepare. Here, the storage unit 103 included in the power consumption prediction apparatus 120 differs from the storage unit 103 included in the power consumption prediction apparatus 100 in that the type information storage unit 1035 is provided instead of the schedule information storage unit 1036. Further, the presentation unit 107 included in the power consumption prediction apparatus 120 has a different function from the presentation unit 107 included in the power consumption prediction apparatus 100. In the second embodiment, the cloud server 520 collects the correspondence information generated by the power consumption prediction apparatus 120, and performs processing such as outlier removal, averaging, and adoption of a median value from the power consumption prediction apparatus 120. Execute according to your request.

  The type information storage unit 1035 stores type information indicating the type to which each electric device belongs. This type is a type for classifying electric devices according to applications. The types include, for example, heating and cooling to which an air conditioner, floor heating, and a fan belong, and cooking to which an IHCH, a microwave, an electric pot, and a rice cooker belong. The function of the type information storage unit 1035 is realized by the function of the flash memory 14, for example.

  In the present embodiment, the electrical devices are classified by type. Then, the prediction unit 105 further predicts the power consumption and the power consumption for each type. Here, the presentation unit 107 presents the power consumption or the power consumption for each type predicted by the prediction unit 105.

  Here, when there is an operation pattern for reducing the power consumption or the amount of power consumption in the type to which the electric device in operation or scheduled to operate belongs, the presentation unit 107 presents the operation pattern to the user as an alternative. It is preferable that the presentation unit 107 presents an alternative to the user when it is necessary to reduce at least one of the power consumption and the power consumption.

  The CPU 11 can determine the type of the management target device based on, for example, the identifier of the management target device acquired from the management target device via the in-home communication interface 17. Further, the CPU 11 may determine the type of the management target device according to, for example, designation by the user via the touch screen 16. The CPU 11 stores type information indicating the type of the management target device in the flash memory 14 (type information storage unit 1035).

  Further, for each type, the CPU 11 compares at least one of power consumption, power consumption, and electricity rate with at least one of the other type, the past performance, and the performance at other demand places. . The screen which presents a comparison result (evaluation result) in FIG. 15 is shown. The screen shown in FIG. 15 presents a reference value, a type evaluation value, a comparison target, and a comparison reference.

  The reference value is a value in the comparison standard to be compared. The reference value is displayed as a straight line representing 100%. The type evaluation value is an evaluation value for each type with respect to the reference value in the comparison standard. The type evaluation value is displayed as a percentage when the standard value is 100%. As the comparison target, one of comparison between types, comparison with the past actual performance data of the demand place itself, and comparison with data acquired from the outside is specified. In the case of comparison between types, for example, the breakdown when the sum in the comparison criteria of all types is 100% is displayed. In the case of comparison with the past actual performance data of the demand area itself, the average value of actual data of any day, the average value of actual data of any period, and the average value of actual data of any day of the week in any period are to be compared. In the case of comparison with externally acquired data, for example, the average value of the nationwide average, the demand places selected from the demand places meeting the specific conditions, and the demand places meeting the specific conditions are compared. The specific condition is, for example, a condition concerning the number of members of the family and the installation place. The comparison criteria include, for example, the electricity charge amount associated with the power consumption, the power consumption amount, and the usage time.

  Next, with reference to FIG. 11, the device usage evaluation process performed by the power consumption prediction apparatus 120 will be described. The device use evaluation process is repeatedly performed, for example, according to an instruction from the user or periodically. The fixed cycle is, for example, 6 hours or 24 hours.

  First, the CPU 11 classifies devices to be managed by type (step S301). The CPU 11 classifies the management target device in accordance with the identifier of the management target device and designation by the user. The CPU 11 stores the type information indicating the classification result in the flash memory 14 (type information storage unit 1035).

  CPU11 will perform evaluation for every classification, if the process of step S301 is completed (step S302). Specifically, the CPU 11 evaluates the power consumption, the power consumption, the electricity charge, or the usage time in each type by comparing with a predetermined comparison target.

  Next, the power consumption prediction process performed by the power consumption prediction apparatus 120 will be described with reference to FIG. In the power consumption prediction process executed by the power consumption prediction apparatus 120, when the predicted power consumption is determined to be equal to or greater than the threshold (step S206: YES), the power saving capacity of the management target device is calculated (step S207) The process is the same as the power consumption prediction process performed by the power consumption prediction apparatus 100 except that the process of presenting an alternative is executed (step S211).

  In step S211, the CPU 11 determines whether or not there is a combination of electric devices which consumes less power or consumes less power in the same type in the table of power consumption characteristics for each type shown in FIG. If it is determined that there is such a combination, this combination is presented to the user. The CPU 11 may display this combination on the touch screen 16 or may display the combination on the terminal device 510.

  In the present embodiment, power consumption or power consumption is presented to the user for each type. Therefore, according to the present embodiment, the user can be motivated to pay attention to the frequency of use, the time of use, and the content of operation when using the electrical device.

  Further, in the present embodiment, a comparison result of the aspect of power consumption in another demand point and the aspect of power consumption in the present demand point is presented via the cloud server 520. For this reason, according to the present embodiment, it is possible to inform the user of relative evaluations of various types of power consumption.

(Embodiment 3)
In the first embodiment, an example has been described in which the power consumption characteristic of the management target device is learned by detecting the operation state of the management target device without actively switching the operation state of the management target device. However, depending on the method of operation of the management target device, such a method may not be able to accurately obtain the power consumption characteristics of each electric device. Therefore, in the present embodiment, an example will be described in which the operation state of the management target device is positively switched to execute the identification operation for accurately determining the power consumption characteristic of each electric device. However, when the operation state of the device to be managed is actively switched, the comfort of the user may be impaired. Therefore, in the present embodiment, a method of executing the identified operation for learning the power consumption characteristics without reducing the convenience of the user will be described.

  First, basic functions of the power consumption prediction apparatus 130 according to the present embodiment will be described with reference to FIG. The power consumption prediction apparatus 130 functionally includes a power information acquisition unit 101, a current value acquisition unit 1021, a predicted value acquisition unit 1022, a storage unit 103, a generation unit 104, a prediction unit 105, a control unit 106, and an identification processing execution unit 108 is provided. Here, storage section 103 included in power consumption prediction apparatus 130 differs from storage section 103 included in power consumption prediction apparatus 100 in that EV power consumption information storage section 1037 and operation history information storage section 1038 are provided. Further, the power consumption prediction apparatus 130 differs from the power consumption prediction apparatus 100 in that the identification processing execution unit 108 is provided instead of the presentation unit 107.

  EV usage information storage unit 1037 stores EV usage information indicating a usage schedule of electric vehicle 420. The EV usage information is, for example, information indicating the scheduled leaving time of the electric vehicle 420 and the scheduled return time. The EV usage information is used when the power consumption prediction apparatus 100, which is a power management apparatus, creates a schedule for precharging before using the electric vehicle 420.

  The operation history information storage unit 1038 stores operation history information indicating a history of all operations performed on the electric device by the user. For example, the operation history information indicates the history of the stop operation of the air conditioner by the remote control, the strength operation of the output of the IHCH by the main body operation, and the batch stop operation of the management target device by the terminal device 510 from outside the house.

  The generation unit 104 generates correspondence relationship information indicating the correspondence between the operating state of the operating electric device and the value of the parameter indicating the operating environment, and the power consumption of the operating electric device.

  The identification process execution unit 108 executes an identification process when there is no user who uses a plurality of electrical devices. The identification process is a process of causing the generation unit 104 to generate correspondence relationship information on the electric device whose operation state has been changed by changing the operation state of the electric device. The identification process execution unit 108 includes the schedule information stored in the schedule information storage unit 1036, the EV usage information stored in the EV usage information storage unit 1037, and the operation history information stored in the operation history information storage unit 1038. Based on it, a period in which the user is absent is determined. The identification process execution unit 108 controls the electrical device for the process for identification via the control unit 106 during a period when the user is absent. FIG. 13 is a diagram showing a daily schedule of the user at the demand place. FIG. 14 is a diagram showing a weekly schedule of the user at the demand place.

  Next, the correspondence relationship information generation processing executed by the power consumption prediction apparatus 130 according to the present embodiment will be described with reference to the flowchart shown in FIG. In the correspondence relationship information generation process executed by the power consumption prediction apparatus 130, the process of step S121 and the process of step S122 are performed after the process of step S104 is completed, and when it is determined YES in step S122, step S123. The processing is the same as the correspondence relationship information generation processing performed by the power consumption prediction apparatus 100 except that the processing in the above is performed.

  When the process of step S104 is completed, the CPU 11 executes presence / absence determination processing to determine whether the user is absent (step S121). The CPU 11 uses the power information stored in the power information storage unit 1031, the operation status information stored in the operation status information storage unit 1034, the schedule information stored in the schedule information storage unit 1036, and the EV usage information storage unit 1037. Whether or not the user is absent is determined based on at least one of the EV utilization information stored in and the operation history information stored in the operation history information storage unit 1038.

  For example, the CPU 11 indicates that the operation history by the user does not operate any electric device for a predetermined period (for example, one hour) by the operation history information, and the power information artificially changes the total power consumption at the demand point Indicates that there is no operation of the electrical equipment based on the operation, and the operation state information indicates that the IHCH assumed to be present when operating is not operating for a fixed period (for example, one hour) If it is determined, it is determined that the user is absent.

  Alternatively, the CPU 11 may determine whether the user is absent based on at least one of the use schedule of the electric vehicle 420 indicated by the EV use information and the leaving schedule indicated by the schedule information. The CPU 11 can appropriately adjust which information of the above-described information is used for the determination. The absence of the user does not necessarily mean that the user is out. In other words, it may mean that the user is in a room other than the room in which the electrical device to be identified is installed. The CPU 11 can determine the presence / absence of the user in room units based on the operation state information and the schedule information.

  When the process of step S121 is completed, the CPU 11 determines whether the user is absent (step S122). When the CPU 11 determines that the user is not absent (step S122: NO), the CPU 11 executes the process of step S105. On the other hand, when determining that the user is not present (step S122: YES), the CPU 11 executes the identification operation (step S123).

  Specifically, the CPU 11 transmits a control signal to the management target device to change the operation state of the management target device, and causes the generation unit 104 to generate correspondence relationship information, and the correspondence relationship information storage unit 1033. Remember. The correspondence information generated by the identification operation is information with high reliability, that is, high accuracy.

  According to the present embodiment, it is possible to obtain highly reliable power consumption characteristics without reducing the comfort of the user.

(Embodiment 4)
In the third embodiment, an example in which highly reliable power consumption characteristics are acquired by the identification operation has been described. However, when the total power consumption can not be acquired from the power measurement apparatus 200 in a short cycle, the update cycle of the total power consumption with respect to the change in the power consumption of the electric device becomes long. As a result, when the identification operation is performed, accurate power consumption characteristics can not be acquired. In this embodiment, when the sampling period of the total power consumption is sufficiently long with respect to the time change of the power consumption of the electric device, a method of acquiring the power consumption characteristic with high accuracy by the identification process will be described. In the present embodiment, an example will be described in which the sampling period of the total power consumption is 30 minutes, the electrical device to be identified is an air conditioner, and the other electrical devices are not operating.

  In the present embodiment, the parameter indicating the operating environment is an elapsed time after operating the operating electric device. Here, the power information acquisition unit 101 acquires power information indicating the total amount of power consumption supplied from the commercial power source 400 to the plurality of electrical devices in a predetermined cycle. The identification process execution unit 108 executes a plurality of identification processes with different times until the power information acquisition unit 101 acquires power information after changing the operation state of any one of the electric devices.

  The graph for demonstrating the method of calculating | requiring the power consumption characteristic of the period corresponded to a difference by shifting operation start time of an air-conditioner in FIG. 18 is shown. Further, FIG. 19 shows the transition of the power consumption per 5 minutes in 30 minutes from 00:00 to 00:30 before shifting the operation start time of the air conditioner. In FIG. 18, FIG. 19 and FIG. 20, the sampling time of the total power consumption is shown at 00:00.

  In FIG. 18, the power amount A0 indicates the power consumption amount from 00:00 to 00:30 when the operation of the air conditioner starts at 00:00, and the power amount A1 starts the operation of the air conditioner at 00:05. It shows the amount of power consumption from 00:00 to 00:30. Further, in FIG. 18, the power amount A2 represents the difference between the power amount A0 and the power amount A1. Further, the electric energy A0 in FIG. 18 corresponds to the sum of the electric energy A3 in FIG. 19 and the electric energy A4 in FIG. The electric energy A1 in FIG. 18 corresponds to the electric energy A5 in FIG.

  Here, since the amount of power A5 and the amount of power A3 represent the amount of power consumption for 25 minutes from the start of operation, when the difference between the values of the parameters affecting the power consumption characteristics such as temperature and humidity is sufficiently small The power consumption A5 and the power consumption A3 which are power consumption for 25 minutes from the operation start in FIG. 19 can be regarded as the same value, and the power consumption for 5 minutes from 25 minutes after the operation start is the power consumption and A2. Look over. As described above, similarly to the power amount A4, it becomes possible to obtain the power consumption in the remaining period of FIG. 19 or every 5 minutes after 00:30, and the power consumption characteristic in units of 5 minutes is obtained.

  In this case, since the power consumption characteristic can be acquired with accuracy according to the fluctuation amount of the operation start time, the power consumption characteristic can be acquired with the same accuracy as the control period regardless of the sampling period of the power consumption. . That is, in the present embodiment, the power consumption characteristic can be acquired more than the accuracy according to the control cycle of the power consumption prediction apparatus 100 regardless of the specification of the power measurement apparatus 200.

(Modification)
As mentioned above, although embodiment of this invention was described, in implementation of this invention, the deformation | transformation and application by various forms are possible.

  In the present invention, which part of the configuration, function, and operation described in the above embodiment is adopted is optional. Further, in the present invention, in addition to the above-described configurations, functions, and operations, further configurations, functions, and operations may be adopted. Further, the configurations, functions, and operations described in the above embodiments can be freely combined.

  For example, which function is to be provided to which of the power consumption prediction apparatus 100 and the cloud server 520 can be adjusted as appropriate. For example, the cloud server 520 may have a function of generating correspondence information and a function of predicting power consumption or power consumption. Also, part of the functions may be provided to the terminal device 510 instead of the cloud server 520. Thus, the power consumption prediction system having the above-described function may be configured by at least one of the power consumption prediction apparatus 100, the terminal device 510, and the cloud server 520. That is, the functional configuration of such a power consumption prediction system includes the power consumption prediction apparatus 100, the terminal device 510, and the cloud server 520 if they are mutually connected via the in-home network 610 or the out-of-home network 620. Any of these may be provided. Such a power consumption prediction system includes, for example, a current value acquisition unit 1021, a power information acquisition unit 101, a generation unit 104, a predicted value acquisition unit 1022, and a prediction unit 105 as a functional configuration. It can be said that these functional configurations are determination systems interconnected via a network. The power consumption prediction system may include the power measurement apparatus 200 instead of the power information acquisition unit 101.

  In the first embodiment, an example in which many of the functions of the power consumption prediction apparatus 100 are realized by software (or firmware), that is, many of the functions of the power consumption prediction apparatus 100 are realized by execution of a program by a processor Explained. In the present invention, such a function may be realized by hardware. In this case, for example, the power consumption prediction apparatus 100 includes a processing circuit instead of the CPU 11. The processing circuit is configured by, for example, a single circuit, a complex circuit, a programmed processor, a parallel programmed processor, an application specific integrated circuit (ASIC), a field programmable gate array (FPGA), or a combination thereof.

  By applying an operation program that defines the operation of the power consumption prediction apparatus 100, 120, 130 according to the present invention or the cloud server 520 to an existing personal computer or information terminal device, the power consumption prediction according to the present invention of the personal computer It is also possible to function as the devices 100, 120, 130 or the cloud server 520.

  Also, the distribution method of such a program is arbitrary, and for example, it is stored by being stored in a computer readable recording medium such as a CD-ROM (Compact Disk Read-Only Memory), a DVD (Digital Versatile Disk), or a memory card. Or may be distributed via a communication network (eg, the Internet).

  The present invention is capable of various embodiments and modifications without departing from the broad spirit and scope of the present invention. In addition, the embodiment described above is for describing the present invention, and does not limit the scope of the present invention. That is, the scope of the present invention is indicated not by the embodiments but by the claims. And, various modifications applied within the scope of the claims and the meaning of the invention are considered to be within the scope of the present invention.

  The present invention is applicable to a power consumption prediction system that predicts the power consumption of an electrical device.

11 CPU, 12 ROM, 13 RAM, 14 Flash Memory, 15 RTC, 16 Touch Screen, 17 In-Home Communication Interface, 18 Out-of-Home Communication Interface, 100, 120, 130 Power Consumption Prediction Device, 101 Power Information Acquisition Unit, 1021 Current Value Acquisition unit, 1022 prediction value acquisition unit, 103 storage unit, 104 generation unit, 105 prediction unit, 106 control unit, 107 presentation unit, 108 identification processing execution unit, 200 power measurement device, 210 sensor, 301, 302, 303, 304 , 305 electric equipment, 400 commercial power supply, 410 power converter, 420 electric car, 510 terminal equipment, 520 cloud server, 521 processor, 522 communication interface, 523 hard disk, 610 home network, 620 home Network, 1031 power information storage unit, 1032 parameter value storage unit, 1033 correspondence relationship information storage unit, 1034 operation status information storage unit, 1035 type information storage unit, 1036 schedule information storage unit, 1037 EV usage information storage unit, 1038 operation history Information storage unit

Claims (9)

A current value acquisition unit that acquires a current value of a parameter that indicates an operating environment of a first electric device among the plurality of electric devices and that affects power consumption of the first electric device ;
Obtain first power information on total power consumption of the plurality of electrical devices before the current value changes, and second power information on total power consumption of the plurality of electrical devices after the current value changes Means for acquiring power information;
Correspondence information indicating the correspondence between the value of the parameter and the power consumption of the first electric device based on the first power information and the second power information in response to the change of the current value Generating means for generating
Predicted value acquisition means for acquiring a predicted value of the parameter;
And prediction means for predicting the power consumption of the first electric device based on the correspondence information generated by the generation means and the predicted value acquired by the predicted value acquisition means.
Power consumption prediction device. The prediction means further predicts the total power consumption or the total power consumption of the plurality of electrical devices when the operating state of any one of the plurality of electrical devices changes.
The power consumption prediction device according to claim 1. When the total power consumption or the total power consumption predicted by the prediction means exceeds a predetermined threshold value, the power saving target electricity is reduced such that the power consumption of the power saving target of the plurality of electrical devices is reduced. Further comprising control means for controlling the device;
The power consumption prediction device according to claim 2. The plurality of electrical devices are classified into a plurality of types,
The prediction means further predicts total power consumption or total power consumption of the plurality of electrical devices;
When the total power consumption or the total power consumption predicted by the prediction means exceeds a predetermined threshold value, the specific type is changed by changing the operation state of the electrical device belonging to the specific type among the plurality of types. Further comprising presentation means for presenting an alternative for reducing the total power consumption or the total power consumption of the electric devices belonging to
The power consumption prediction device according to any one of claims 1 to 3. The generation means generates correspondence information indicating a correspondence between an operation state of the first electric device, a value of the parameter, and power consumption of the first electric device.
In the absence of a user who uses the plurality of electrical devices, the generating unit is configured to generate correspondence information on the electrical devices to be identified by changing the operation state of the electrical device to be identified among the plurality of electrical devices. Further comprising identification processing execution means for executing identification processing to be generated by
The power consumption prediction device according to any one of claims 1 to 4. The parameter is an elapsed time after operating the first electrical device,
The power information acquisition means acquires, in a predetermined cycle, power information indicating total power consumption consumed by the plurality of electric devices.
The identification process execution means executes a plurality of identification processes having different times until the power information acquisition means acquires the power information after changing the operation state of the electrical device to be identified.
The power consumption prediction device according to claim 5. A current value acquisition unit that acquires a current value of a parameter that indicates an operating environment of a first electric device among the plurality of electric devices and that affects power consumption of the first electric device ;
The first total power consumption which is the total power consumption of the plurality of electrical devices before the current value changes, and the second total power consumption which is the total power consumption of the plurality of electrical devices after the current value changes And power measuring means for measuring
Correspondence indicating the correspondence between the value of the parameter and the power consumption of the first electric device based on the first total power consumption and the second total power consumption in response to the change of the current value Generation means for generating relationship information;
Prediction means for predicting power consumption of the first electric device based on the correspondence information generated by the generation means and the predicted value of the parameter;
Power consumption forecasting system. Acquiring a current value of a parameter indicating an operating environment of a first electric device among the plurality of electric devices, the parameter affecting power consumption of the first electric device ;
Obtain first power information on total power consumption of the plurality of electrical devices before the current value changes, and second power information on total power consumption of the plurality of electrical devices after the current value changes And
Correspondence information indicating the correspondence between the value of the parameter and the power consumption of the first electric device based on the first power information and the second power information in response to the change of the current value Generate
The power consumption of the first electric device is predicted based on the generated correspondence information and the predicted value of the parameter.
Power consumption forecasting method. Computer,
A current value acquisition unit that acquires a current value of a parameter that indicates an operating environment of a first electric device among the plurality of electric devices and that affects power consumption of the first electric device ;
Obtain first power information on total power consumption of the plurality of electrical devices before the current value changes, and second power information on total power consumption of the plurality of electrical devices after the current value changes Power information acquisition means,
Correspondence information indicating the correspondence between the value of the parameter and the power consumption of the first electric device based on the first power information and the second power information in response to the change of the current value Means for generating
Predicted value acquisition means for acquiring a predicted value of the parameter;
It functions as a prediction unit that predicts the power consumption of the first electric device based on the correspondence information generated by the generation unit and the predicted value acquired by the predicted value acquisition unit.
program.

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