JP2014078137A - Information processing device, information processing method, and program - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide an information processing technique for accurately predicting power consumption of an apparatus.SOLUTION: An information processing device comprises: a power consumption reading unit that reads a past power consumption value before a prediction target date from a measured value storage unit storing the past power consumption value; a statistical processing unit that calculates a statistical value obtained by statistically processing the past power consumption value or its time dependency; and a power consumption prediction unit that calculates a power consumption value on the prediction target date or its time dependency on the basis of a period dependency of the statistical value calculated by the statistical processing unit.

Description

  The present invention relates to information processing technology, and more particularly, to information processing technology related to power consumption prediction of equipment.

In recent years, with the heightened awareness of energy saving, energy saving of devices such as home appliances has been promoted.
For example, in recent years, a system (for example, a home energy management system (HEMS)) capable of collectively controlling a plurality of devices such as home appliances and house equipment has been spreading. For example, in the system described in Patent Document 1 below, network home appliances such as an air conditioner and an illumination lamp connected to a home network are operated on a screen displayed by a personal computer. In response to the operation, the ASP server specifically controls the operation of the network home appliance. On the screen of the personal computer, mode buttons corresponding to life scenes such as “Odekake”, “Good night”, “Okaeri” are provided as convenient buttons. For example, when an “outing” button is operated, the air conditioner is stopped accordingly and the illumination lamp is turned off. In this manner, different types of network home appliances can be collectively controlled in a certain mode.

On the other hand, energy saving of individual devices such as home appliances is also progressing. For example, a conventional home air conditioner (hereinafter referred to as an “air conditioner”) normally selects a desired operation mode, air volume, indoor set temperature, and the like using a remote control device. Moreover, at least one radiation temperature sensor is attached to the outlet of the indoor unit of the air conditioner, and the indoor set temperature is corrected by detecting the floor temperature or the occupant's temperature (see Patent Document 2 below).
In order to save energy during cooling or heating operation (hereinafter referred to as “energy saving”) for the purpose of effective use of energy, an energy saving temperature range is set during cooling or heating operation, and energy is saved by a remote control device. When the operation mode is selected, a proposal has been made to effectively use energy by setting the indoor set temperature to any temperature within the energy saving temperature range and operating the air conditioner (see Patent Document 3). .

  In addition, a supply and demand power amount calculation system described in Patent Document 4 includes a supply power amount calculation unit that calculates a supply power amount per unit time that is supplied from a PPS to an electric power company, and a unit of a customer that is contracted with the PPS. A demand power amount calculation unit that calculates a demand power amount for each hour, and a power demand amount calculation unit that calculates a supply and demand power deviation amount based on a difference between a supply power amount per unit time and a total demand power amount per unit time And a charging unit that charges an excess charge based on the deviation of the supply and demand power. The demand power amount calculation unit calculates a demand power amount for each consumer for one month by using a standard load curve that is a daily estimated value of the power amount per unit time consumed by each consumer. Thus, the supply / demand deviation is calculated without installing an interval meter or the like at the time of connection supply. Further, based on the calculated supply and demand power deviation amount, the supply and demand power deviation amount in power operation can be monitored.

JP 2006-350819 A JP-A-6-82075 JP 2006-125669 A Japanese Patent Laying-Open No. 2005-224023

For example, in order to save energy in home appliances such as an air conditioner and a lighting device, it is important to predict power consumption for each home appliance.
In the prior arts disclosed in Patent Documents 1 to 3, there is no disclosure regarding prediction of power consumption. In Patent Literature 4, power operation is predicted, but there is no disclosure regarding specific prediction for each device.
As described above, when home appliances are controlled in consideration of energy saving, it is important to predict power consumption for each device. In addition, by controlling the equipment using power consumption prediction technology, it is possible to accurately obtain the power supply purchaser when using power liberalization and the use schedule of private power generation when using private power generation together. Technology that can save on electricity bills becomes important.
An object of the present invention is to provide an information processing technique for accurately predicting power consumption of a device.

  According to one aspect of the present invention, a power consumption reading unit that reads a power consumption value past the prediction target date from a measured value storage unit that stores the past power consumption value, and the past power consumption value or the A statistical processing unit that obtains statistical values obtained by statistically processing time dependence, and a consumption that obtains the power consumption value of the prediction target day or its time dependence based on the period dependence of the statistical values obtained by the statistical processing unit An information processing apparatus including an electric power prediction unit is provided.

  The statistical processing unit uses the same day of the week before the prediction target date, for example, the same day of the week as a reference day, and calculates the first power consumption value of the past x days of the same day before the reference date or its time dependency. Statistically processing the first statistical value processed statistically and the second power consumption value of y day (y> x) in a period longer than the x day of the same day before the reference date or its time dependence And a coefficient calculating unit for calculating a weighting coefficient between the first statistical value and the second statistical value, wherein the power consumption prediction unit includes the weighting The statistical value obtained by the statistical processing unit based on the coefficient is the power consumption value on the prediction target day or its time dependence.

The power consumption reading unit uses the first day of the week before the reference date and the same day of the week as a reference date as a reference date, the first previous power consumption value of the same day before the reference date or its time dependency, and before the reference date. The second power consumption value or its time dependency of the most recent x days (x is a positive value) before the past prediction target date, and y days (y> x) in a period longer than the x days before the prediction target date The third power consumption value or the time dependency thereof, and the statistical processing unit reads the first power consumption value or the time dependency thereof statistically processed by the first statistical value and the second power consumption value. A second statistical value obtained by statistically processing the power consumption value or its time dependence; and a third statistical value obtained by statistically processing the third power consumption value or the time dependence thereof; and A factor for obtaining a weighting coefficient of the first statistical value, the second statistical value, and the third statistical value Has a calculation unit, the power estimation unit may be a dependent consumption values or time of the prediction target day statistics obtained by the statistical processing unit based on the weighting factor.
The first statistical value is obtained in the period immediately before the prediction target date, and the second statistical value is obtained on the same day of the week more than one week before the prediction target day. It is characterized by being.
The statistical value may be an average value, and the statistical value may be an extrapolated value.

The present invention is an apparatus including the information processing apparatus described above.
The present invention compares the predicted power consumption value predicted by the information processing apparatus described above with a reference (predetermined) power consumption value, and the predicted result is a reference value based on the comparison result. The power consumption value is controlled to be lower than the power consumption value.

  The present invention is a device control system that performs device control or information processing related to the device control based on a power consumption prediction result by the information processing apparatus described above.

  According to another aspect of the present invention, a power consumption reading step of reading a power consumption value past the prediction target date from a measured value storage unit that stores the past power consumption value, and the past power consumption value or A statistical processing step for obtaining a statistical value obtained by statistically processing the time dependence, and a power consumption value of the prediction target day or a time dependence thereof is obtained based on a period dependence of the statistical value obtained in the statistical processing step. There is provided an information processing method comprising a power consumption prediction step.

  The present invention may be a program for causing a computer to execute the information processing method described above, or a computer-readable recording medium for recording the program.

  According to the present invention, it is possible to accurately predict the power consumption of a device.

It is a functional block diagram which shows the example of 1 structure of the air-conditioner apparatus provided with the information processing apparatus by the 1st Embodiment of this invention. It is a functional block diagram which shows one structural example of the information processing part which performs the process by the power consumption prediction program by the 1st Embodiment of this invention. It is a figure which shows the prediction target day of power consumption value, and the calculation target day used for prediction with a calendar. It is a flowchart figure which shows the flow of a process in an information processing part. It is a figure which shows the example in the case of calculating | requiring the time dependence of a power consumption value instead of the power consumption value shown in FIG. It is a flowchart figure which shows the flow of a process in an information processing part. It is a flowchart figure which shows the flow of the process which calculates | requires the time dependence of a power consumption value instead of a power consumption value. It is a flowchart figure which shows the flow of a process in the information processing part by the information processing technique by the 3rd Embodiment of this invention. It is a flowchart figure which shows the flow of the process which calculates | requires the time dependence of a power consumption value instead of the power consumption value of FIG. It is a figure which shows an example of the computer processing in the air-conditioner control by the information processing technique by the 4th Embodiment of this invention, and is a figure which shows the example of control using the said power consumption estimated value. It is a figure which shows the example of 1 structure of the household appliance control system using the power consumption prediction technique of the household appliance by the 5th Embodiment of this invention.

Hereinafter, an information processing technique related to prediction of power consumption of home appliances and the like according to an embodiment of the present invention will be described in detail with reference to the drawings. In the following, information processing technology related to prediction of power consumption of home appliances will be described by taking an air conditioner device as an example for home appliances, but the same technology is also applied to other devices such as lighting devices, refrigerators, electric vehicles, etc. Can be used.
The present embodiment relates to information processing technology related to power consumption prediction, focusing on the fact that the power consumption of a device varies greatly depending on the user's lifestyle, and that the power consumption of the device is particularly time-dependent and day-dependent. It is.
FIG. 1 is a functional block diagram illustrating a configuration example of an air conditioner apparatus including an information processing apparatus according to the first embodiment of the present invention. As shown in FIG. 1, an air conditioner A including an air conditioner control device 5 according to the present embodiment includes an indoor unit 1, an outdoor unit 3, a remote control device 17, and a power consumption measuring device 18. . A remote control signal for performing on / off control and temperature control of the device from the remote control device 17 is received by the remote control signal light receiving unit 29. The power consumption measuring device 18 is, for example, a device that is arranged between a home appliance such as a smart tap and an indoor outlet, calculates power consumption from the detected voltage and current, and transmits the result, for example, by wireless communication. Can be realized.

The air conditioner control device 5 includes a CPU (control unit) 21, a RAM (main storage device) 23, and a ROM (nonvolatile memory) 27. In a ROM (non-volatile memory) 27, a measured value storage unit 27a for storing measured values measured by the power consumption measuring device 18, an automatic operation program 27b for automatically operating the air conditioner, and a power consumption prediction program 27c. Etc. are stored. The power consumption prediction program 27c is a program that causes the CPU 21 to function as an information processing unit 21a that performs information processing for predicting power consumption.
The measured value storage unit 27a stores the time change of the power consumption value measured by the power consumption meter 18 and the like together with the year, month, day, day of the week, for example, in units of 24 hours. The automatic operation program 27b is a control program for controlling the air conditioner device. For example, at the start of the air conditioner start-up, it is determined whether the air conditioner is operated in the energy-saving operation mode “eco-automatic operation” and “air-conditioning” or “heating” depending on the button pressed by the user. If the judgment of “cooling” and “heating” is the same, the air conditioner operates in “eco-automatic operation”. On the other hand, when the judgment of “cooling” and “heating” depending on the button pressed by the user is different from the judgment of “cooling” and “heating” judged when the air conditioner operates in “eco-automatic operation” Operates in the “cooling” or “heating” state in which the user's judgment (operation) is prioritized. In addition, when activated by the “automatic” button, “eco-automatic operation” is performed. For example, a table showing what kind of driving is performed according to the relationship between the set temperature at the start of driving and the eco-automatic driving range (determination of driving state when eco-automatic driving is performed). Controlled based on. The operation control of the air conditioner can correspond to various known methods. In the above description, eco-automatic driving has been described as an example. However, auto-driving is not specialized for ecology, and may be automatic driving of a general air conditioner, and is not limited to eco-driving.
The power consumption prediction program 27c and the processing contents based on it will be described in detail below.

FIG. 2 is a functional block diagram showing a configuration example of the information processing unit 21a that performs processing by the power consumption prediction program 27c according to the present embodiment. FIG. 3 is a diagram showing a prediction date of the power consumption value and a calculation date used for prediction in a calendar. FIG. 4 is a flowchart showing the flow of processing in the information processing section 21a. The information processing apparatus may be connected to the air conditioner apparatus in a wired or wireless manner.
The information processing unit 21a includes a power consumption reading unit 21a-1 that reads out a power consumption value, a statistical processing unit 21a-2 that performs statistical processing related to information processing of the power consumption value, and a statistic that determines the dependency of different power consumption values. Based on the value, the weighting coefficient of the dependency is obtained, that is, the coefficient calculating unit 21a-3 for obtaining the weighting coefficient of the dependency of the period or the different period, and the power consumption value as a result of the statistical processing and the coefficient calculation. And a power consumption prediction unit 21a-4 that performs power consumption value prediction based on the power consumption prediction unit 21a-4.

The prediction target day of the power consumption value is, for example, July 28 (Saturday), and the power consumption value measured in the previous period is used for the prediction. The extent of going back to the past can be arbitrarily selected, for example, one month ago. The prediction reference date is, for example, July 21 immediately before the same day of the week as the power consumption value prediction target date.
When the information processing is started (step S31), the power consumption reading unit 21a-1 predicts the predicted target date (July 28) from the measured value storage unit 27a that stores the power consumption value of the day together with the date and day of the week. The first past power consumption value of the latest x days and the second power consumption value of y days longer than that are read from the measured value storage unit 27a (step S32). For example, if the reference date of the past period for obtaining the power consumption value for obtaining the coefficient is July 21, the first power consumption value is the statistics for the first period until July 14-20. The second power consumption value is a statistical value of the second period from July 1 to July 20. Since there is a day of the week dependency, it is preferable to include the same day of the week as the prediction target day in any period. Further, it is preferable that both the first period and the second period be m weeks (m is a positive number of 1 or more) in units of one week.
The statistical processing unit 21a-2 first and second statistical values obtained by statistically processing the first power consumption value in the first period and the second power consumption value in the second period, respectively. (For example, an average value) is obtained (step S33). The coefficient calculation unit 21a-3 obtains the first statistical value, the second statistical value, and the weighting coefficient obtained by the statistical processing unit 21a-2 (step S34). If the statistical value is an average value, the first statistical value and the second statistical value are obtained by obtaining an average of 7 days and 20 days in the first period. Next, in step S35, the power consumption prediction unit 21a-4 obtains the power consumption value of the prediction target day using the statistical value and the weighting coefficient.

In step S34, the coefficient for obtaining the prediction result can be obtained as follows (in the example of the calendar in FIG. 3, the coefficient for the reference date (July 21) is used as a reference).
A (t): Average power consumption value for the past second period (all periods, for example, from July 1 to July 20 one day before the reference date)
C (t): Average power consumption value in the past first period (a certain period, for example, from July 14 to 20 days one day before the reference date)
D (t): Power consumption value on the target date for fitting the prediction result (for example, July 21, one week before the prediction target date, reference date)
D (t) = aA (t) + cC (t) (1)
The combination of the coefficients (a, c) with which the value D (t) in the equation (1) is closest is calculated using, for example, a bisection method. Here, t is time. The calculated coefficients a and c and the power consumption value D ′ (t on July 28, which is the prediction target date, from A ′ (t) and C ′ (t) for the prediction target date (Saturday, July 28) ) Is calculated.
D '(t) = aA' (t) + cC '(t) (2)
It should be noted that general statistical processing such as excluding days with prominent values or changing the period depending on the obtained value can be applied to the above period.
In general, the most recent period (first period) is more dependent than the second period.
Thus, the power consumption value can be accurately predicted by performing the power prediction depending on the period affecting the lifestyle.

FIG. 5 is a diagram illustrating an example of obtaining time dependency of the power consumption value instead of the power consumption value illustrated in FIG. 4.
When the information processing is started (step S41), the power consumption reading unit 21a-1 reads, for example, the prediction target date from the measurement value storage unit 27a that stores the time dependency of the power consumption value of the day together with the date and the day of the week. The time dependence of the first power consumption value of the most recent x days and the time dependence of the second power consumption value of the y days in a longer period are read (step S42).
The statistical processing unit 21a-2 performs statistical processing on the time dependence of the first and second power consumption values to obtain the first and second statistical values (time dependence) (step S43). Here, as an average value, an average of 7 days as the first period and 20 days as the second period is obtained. Next, the coefficient calculation unit obtains a weighting coefficient between the first and second statistical values (time-dependent) (step S44). Next, in step S45, using the weighting coefficient, the time dependency of the power consumption value on the prediction target day is obtained, and the process is terminated (step S46).

In addition, as a statistical value, an example (for example, obtaining time dependency) in which a statistical value is obtained by extrapolation value can be used instead of the average value shown in FIGS. 4 and 5.
In this case, an extrapolated value is obtained as a statistical value instead of the average value. That is, for example, the length of the period is plotted on the horizontal axis and the statistical value is plotted on the vertical axis, and extrapolated to shorten the period (for example, the period between the prediction target days is 0). In step S45, the time dependence of the power consumption value of the prediction target day is obtained using a weighting coefficient.
The power consumption value depending on the length of the measurement period with respect to the usage status of the user's air conditioner device can be obtained by any of the above processes.

Next, an information processing technique according to the second embodiment of the present invention will be described. A basic configuration example of the information processing apparatus is the same as that shown in FIGS. FIG. 3 is also used for explanation. In the first embodiment, the power consumption value is predicted based on the weighting of the power consumption value that depends on the length of the power measurement period and does not depend on the day of the week. In the present embodiment, an example in which the power consumption value is obtained based on the weighting of the power consumption value that depends on the length of the power measurement period and also on the day of the week will be described.
FIG. 6 is a flowchart showing the flow of processing in the information processing section 21a according to this embodiment.
The prediction target day of the power consumption value is, for example, July 28 (Saturday), and the power consumption value measured in the period before the reference date is used for the prediction. The extent of going back to the past can be arbitrarily selected, for example, one month ago.

  When the information processing is started (step S51), the power consumption reading unit 21a-1 predicts the predicted target date (July 28) from the measured value storage unit 27a that stores the power consumption value of the day together with the date and day of the week. The past power consumption values before the reference date on the same day are read from the measured value storage unit 27a (step S52). For example, with the prediction target day (July 28 (Saturday)) and, for example, the same day one week ago (July 21 (Saturday)) as the reference day, the first period x on the same day prior to this reference day x The first power consumption value in the past of the day (for example, two days on July 14 and July 7) and the second power longer than x days before the reference date (Saturday, July 21) The past second power consumption value of period y (for example, four days of July 14, July 7, June 30, and June 23) is read from the measured value storage unit 27a.

The statistical processing unit 21a-2 obtains a first statistical value obtained by statistically processing the first power consumption value and a second statistical value (for example, an average value) obtained by statistically processing the second power consumption value (step). S53). The coefficient calculation unit 21a-3 obtains a weighting coefficient between the first statistical value and the second statistical value (step S54). Then, the power consumption prediction unit 21a-4 uses the weighting coefficient to calculate the power consumption of the prediction target day based on the statistical value obtained by the statistical processing unit 21a-2 and the coefficient obtained by the coefficient calculation unit 21a-3. A value is obtained (step S55). Thereby, the process is terminated (step S56).
The coefficient for obtaining the prediction result can be obtained as follows (in the example of the calendar in FIG. 3, data up to the 20th is used and compared with the 21st).
A (t): Average power consumption value for the first period of the specified day in the past (for example, two days on July 14 and July 7)
B (t): Average power consumption value in the second period of the past designated day of the week (for example, July 14, July 7, June 30 and June 23)
D (t): Power consumption value on the target date (July 21 one week before the forecast date) for fitting the forecast results
D (t) = aA (t) + bB (t) (1)
(1) A combination of coefficients (a, b) with which the value D (t) in the equation is closest is calculated. Here, t is time. The calculated coefficients a and b and the power consumption value D ′ (t on July 28, which is the prediction target date, from A ′ (t) and B ′ (t) for the prediction target date (Saturday, July 28). ) Is calculated.
D '(t) = aA' (t) + bB '(t) (2)

Note that, for the above-described period and designated day of the week, general statistical processing such as excluding days with prominent values or changing the period depending on the obtained value can be applied.
Similarly to the case of FIG. 5, as shown in FIG. 7, the time dependence of the power consumption value can be obtained instead of the power consumption value (steps S61 to S66). In this case, the statistical value and the coefficient value may be obtained for each time zone or every hour, or after dividing into on time and off time using threshold power consumption, etc., the on period and off time Statistical processing may be performed during the period.
Further, an average value may be used as the statistical value, and an extrapolated value may be used as the statistical value instead.
As described above, according to the present embodiment, the weighting of the statistical value having the day of the week dependency and the statistical value not dependent on the day of the week is obtained, and this weighting is performed when obtaining the power consumption value of the prediction target day. By using it, it is possible to perform prediction in consideration of day-of-week dependency.

Next, an information processing technique according to the third embodiment of the present invention will be described. The basic configuration is the same as in FIGS. FIG. 3 is also used for explanation.
FIG. 8 is a flowchart showing the flow of processing in the information processing section 21a according to this embodiment.
The process is started (step S71), from the measured value storage unit 27a that stores the daily power consumption value together with the date and day of the week, the power consumption reading unit 21a-1 selects the same day one week before the prediction target day. As a reference date, the first power consumption value in the past on the same day of the week before the reference date and the second consumption on the last x days (x is a positive value) before the prediction target date before the reference date. The power value and the third power consumption value on a day y (y> x) longer than the x day before the prediction target date are read (step S72). Next, the statistical processing unit 21a-2 statistically processes the first power consumption value, the second statistical value that statistically processes the second power consumption value, and the third A third statistical value obtained by statistically processing the power consumption value is obtained (step S73). Next, in step S74, the coefficient calculation unit 21a-3 obtains a weighting coefficient for the first statistical value, the second statistical value, and the third statistical value. In step S75, the power consumption prediction unit 21a-4 obtains the power consumption amount of the prediction target day based on the weighting coefficient (end in step S76).

Constants a, b, and c for obtaining the prediction result are obtained. (In the following calendar example, the coefficient for obtaining the de-measurement results up to the 20th can be obtained as follows (in the calendar example of FIG. 3, the data up to the 20th is used and compared with the 21st).
A (t): Average power consumption value for the past second period (all periods, for example, from July 1 to July 20 one day before the reference date)
B (t): Average power consumption value in the past designated day of the week (for example, July 7 and July 14)
C (t): Average power consumption value in the past first period (a certain period, for example, from July 14 to 20 days one day before the reference date)
D (t): Power consumption value on the target day (the day one week before the forecast date) for fitting the forecast results
D (t) = aA (t) + bB (t) + cC (t)
Calculate the combination of a, b, and c that is closest. Here, t is time.
The power consumption value is calculated from the constants a, b, c and A (t), B (t), and C (t) for the prediction target date. In FIG. 3, data up to the 27th day is used and the 28th day is predicted.
Note that, for the above-described period and designated day of the week, general statistical processing such as excluding days with prominent values or changing the period depending on the obtained value can be applied.

As shown in FIG. 9, the time dependence of the power consumption value can be obtained instead of the power consumption value of FIG. 8 (steps S81 to S86). In this case, the statistical value and the coefficient value may be obtained for each time zone or every hour, or after dividing into on time and off time using threshold power consumption, etc., the on period and off time Statistical processing may be performed during the period.
Further, an average value may be used as the statistical value, and an extrapolated value may be used as the statistical value instead.
As described above, according to the present embodiment, the weighting between the relatively recent value and the statistical value having a longer value among the day-of-week dependencies is obtained, and this weighting is calculated based on the consumption of the prediction target day. By using it when obtaining the power value, it is possible to perform prediction in consideration of day-of-week dependency and its period dependency.
In addition, you may make it perform the information processing in said each embodiment with portable terminals, such as a smart phone. A mobile terminal such as a smartphone may be used instead of the remote control device, or may be used as a control device including a GUI.

Next, an information processing technique according to the fourth embodiment of the present invention will be described. The basic configuration using the first to third embodiments and any one of the predicted values is the same as that shown in FIGS. FIG. 3 is also used for explanation. FIG. 10 is a diagram illustrating an example of computer processing in the air conditioner control according to the present embodiment, and is a diagram illustrating a control example using the predicted power consumption value. As shown in FIG. 10, in step S201, a predicted value of power consumption value and a budget (such as current electricity bill) are obtained, and in step S202, it is determined whether or not the budget is exceeded. In the case of Yes, it progresses to step S203, changes the operation schedule of an air-conditioner apparatus (so that it may be set to control which makes a power consumption value low by automatic driving etc.) from a power consumption prediction result, and returns to step S202. In the case of No in step S202, the process proceeds to step S204, and it is determined whether or not the minimum usage budget is insufficient. In the case of Yes, it progresses to step S203, and in No, a power consumption prediction result is set to an apparatus operation schedule as it is. Compared with the budget, how to control from the predicted power on the target day, how much is accumulated in the storage battery (use of nighttime power), based on the estimated power consumption, (Energy saving) mode is provided. Control by increasing / decreasing the ON time and control by adjusting the threshold value may be performed.
With the above control, appropriate device control can be performed based on the power prediction.

Hereinafter, an information processing technique according to the fifth embodiment of the present invention, in which the power prediction technique according to the above-described embodiment is used for a home appliance control system (HEMS) or the like, will be described.
FIG. 11 is a functional block diagram showing a configuration example of the HEMS according to the present embodiment.
As shown in FIG. 11, the power consumption from each home appliance A to D is measured by a “plug-type power sensor” 18 or a “CT sensor” 18 a attached to a distribution board, and the measurement result is obtained by the HEMS HEMS. Provide to the management unit 30. In addition, the HEMS management unit 30 performs “control of home appliances” for controllable home appliances A to D and the like, and displays advice on a television or a small monitor for home appliances that cannot be controlled.
The HEMS management unit 30 includes at least a home appliance power consumption prediction unit 31 (21 a-4) and a home appliance scheduler 33. The home appliance power consumption prediction unit 31 has a configuration corresponding to the power consumption prediction unit 21a-4 of FIG. Or the structure which acquires collectively the prediction information from the power consumption estimation part 21a-4 of FIG. 2 and uses it for control may be sufficient.
Further, FIG. 11 includes an advice generation unit 35, a visualization / control user interface unit 37, and an energy storage energy device management unit 41.
The home appliance power consumption prediction unit 31 analyzes a user's life pattern from the power usage history in the home, and predicts the future power consumption value in the home together with the season / weather and input information of various sensors. .
The home appliance scheduler unit 33 operates according to the home appliance power consumption prediction unit 31 and the like, and the home appliance operation state (level shift = On / Off, temperature, brightness, etc.) and operation timing (time shift, that is, change of use time zone). Scheduling (planning). Controllable home appliances are controlled according to the scheduling result and the like.
The advice generation unit 35 generates advice according to the scheduling result and displays advice to the user via the visualization / control user interface 37.
The energy storage energy equipment management unit 41 performs energy management of the energy storage energy storage equipment 50 such as the solar battery 51, the storage battery 53, and the electric vehicle 55, and also plans for the overall energy consumption of the home appliances AD in the home. Generate information. The plan information may be a target value for each time zone of energy consumption of the home appliance AD, or may be an upper limit value or a lower limit value.
In the present HEMS system, the home appliance scheduler 33 generates the operation state and timing of the home appliance based on the two information of the power consumption prediction result of the home appliance AD and the plan information. The information to be generated by the scheduler section 33 of the home appliance is not limited to these.

In the above-described embodiment, the configuration and the like illustrated in the accompanying drawings are not limited to these, and can be appropriately changed within a range in which the effect of the present invention is exhibited. In addition, various modifications can be made without departing from the scope of the object of the present invention. Each component of the present invention can be arbitrarily selected, and an invention having a selected configuration is also included in the present invention.
For example, in order to be able to arbitrarily set the period for obtaining the statistical value and the day of the week, for example, the target date, the period for obtaining the first and second statistical values, the specified day of the week, etc. are arbitrarily set from the GUI as shown in FIG. You may comprise so that expected power consumption may be displayed, setting. It may be provided on the remote control device side.
For example, in order to be able to arbitrarily set the period for obtaining the statistical value and the day of the week, for example, the target date, the period for obtaining the first and second statistical values, the specified day of the week, etc. are arbitrarily set from the GUI as shown in FIG. You may comprise so that expected power consumption may be displayed, setting. It may be provided on the remote control device side.
In addition, a program for realizing the functions described in the present embodiment is recorded on a computer-readable recording medium, and the program recorded on the recording medium is read into a computer system and executed to execute processing of each unit. May be performed. The “computer system” here includes an OS and hardware such as peripheral devices.
Further, the “computer system” includes a homepage providing environment (or display environment) if a WWW system is used.

  The “computer-readable recording medium” refers to a storage device such as a flexible medium, a magneto-optical disk, a portable medium such as a ROM and a CD-ROM, and a hard disk incorporated in a computer system. Furthermore, the “computer-readable recording medium” dynamically holds a program for a short time like a communication line when transmitting a program via a network such as the Internet or a communication line such as a telephone line. In this case, a volatile memory in a computer system serving as a server or a client in that case, and a program that holds a program for a certain period of time are also included. The program may be a program for realizing a part of the above-described functions, or may be a program that can realize the above-described functions in combination with a program already recorded in a computer system. At least a part of the functions may be realized by hardware such as an integrated circuit.

  The present invention can be used for power prediction of equipment.

A ... Air conditioner device, 1 ... Indoor unit, 3 ... Outdoor unit, 17 ... Remote control device, 18 ... Power consumption measuring device, 21 ... CPU (control unit), 21a ... Information processing unit, 21a-1 ... Power consumption reading unit , 21a-2 statistical processing unit, 21a-3 ... coefficient calculation unit, 21a-4 ... power consumption prediction unit, 21b-1 ... coefficient calculation unit, 23 ... RAM (main storage device), 27 ... ROM (non-volatile memory) , 27a ... a measured value storage unit, 27b ... an automatic operation program, 27c ... a power consumption prediction program.

Claims (12)

A power consumption reading unit that reads a power consumption value past the prediction target date from a measured value storage unit that stores the past power consumption value;
A statistical processing unit for obtaining a statistical value obtained by statistically processing the past power consumption value or its time dependence;
An information processing apparatus comprising: a power consumption prediction unit that obtains a power consumption value of the prediction target day or a time dependency thereof based on a period dependency of the statistical value obtained by the statistical processing unit. The statistical processing unit statistically processes the first power consumption value of the past x days on the same day before the reference date or the time dependency thereof, with the same day of the week prior to the prediction date as a reference day. A second statistically processed second power consumption value or time dependency thereof on a first statistical value and y day (y> x) of a day longer than the x day of the same day prior to the reference date Statistic and
And a coefficient calculation unit for obtaining a weighting coefficient between the first statistical value and the second statistical value,
The information processing apparatus according to claim 1, wherein the power consumption prediction unit sets a statistical value obtained by the statistical processing unit based on the weighting coefficient as a power consumption value of a prediction target day or a time dependency thereof. . The power consumption readout unit is
With the prediction date and the same day of the week before the reference date as the reference date, the past first power consumption value of the same day prior to the reference date or its time dependency, and the previous prediction target date before the reference date The second power consumption value or its time dependence of the latest x days (x is a positive value) and the third power consumption value of y (y> x) days longer than the x days before the prediction target date or its Time dependent, read out,
The statistical processing unit
A first statistical value obtained by statistically processing the first power consumption value or its time dependency; a second statistical value obtained by statistically processing the second power consumption value or its time dependency; A third statistical value obtained by statistically processing the power consumption value of 3 or the time dependence thereof, and
Furthermore, it has a coefficient calculation part which calculates | requires the weighting coefficient of the said 1st statistics value, the said 2nd statistics value, and the said 3rd statistics value, The said power consumption estimation part is based on the said weighting coefficient, and the said statistics The information processing apparatus according to claim 1, wherein the statistical value obtained by the processing unit is a power consumption value of the prediction target day or a time dependency thereof.   The information according to claim 3, wherein the first statistical value is obtained in a period immediately before the prediction target date, and the second statistical value is obtained on the same day of the week more than one week before the prediction target date. Processing equipment.   The information processing apparatus according to claim 1, wherein the statistical value is an average value.   The information processing apparatus according to claim 1, wherein the statistical value is an extrapolation value.   The apparatus provided with the information processing apparatus of any one of Claim 1-6.   The power consumption prediction value predicted by the information processing apparatus according to any one of claims 1 to 6 is compared with a reference power consumption value. Based on the comparison result, the prediction result indicates a reference value. A device characterized by performing control to lower the power consumption value if it exceeds the upper limit.   A device control system that performs device control or information processing related to the device control based on a power consumption prediction result by the information processing device according to any one of claims 1 to 6. A power consumption reading step of reading a power consumption value past the prediction target date from a measured value storage unit that stores the past power consumption value;
A statistical processing step for obtaining a statistical value obtained by statistically processing the past power consumption value or its time dependence;
An information processing method comprising: a power consumption prediction step for obtaining a power consumption value of the prediction target day or a time dependency thereof based on a period dependency of the statistical value obtained in the statistical processing step.   A program for causing a computer to execute the information processing method according to claim 10. The computer-readable recording medium which records a program in Claim 11.

Images