JP2014036564A - Forecast data management device, and forecast data management method - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a forecast data management device capable of utilizing information on power consumption even when demand response control is activated.SOLUTION: A forecast data management device receives designation of a target period used for forecasting power demand and a reading request for forecast data as input; refers to DR activation information stored in a storage unit; determines whether or not a power actual value on a date in the target period is a value on a date when demand response control was activated; outputs the power actual value to an operation management device as forecast data, in the case where the power actual value on the date in the target period is the value at time when the demand response control was not activated; and outputs a value calculated with respect to power used in the case where the demand response control is not activated on the date to the operation management device as forecast data, in the case where the power actual value on the date in the target period is the value at time when the demand response control was activated.

Description

  The present invention relates to a prediction data management apparatus and a prediction data management method.

There is a demand response (DR) as a method for performing an effective power peak cut. DR is a control technology in which a customer reduces power consumption in response to demand in the power network when supply and demand are tight. In order to effectively implement this DR, it is required to perform power demand prediction (hereinafter referred to as prediction) for each customer.
As a technique for performing the power demand prediction, for example, there is a technique disclosed in Patent Document 1 below. In the technology of this Patent Document 1, an average demand amount of power at the same time in the past is calculated, a deviation between the average demand amount and an actual demand amount is calculated, and a prediction formula based on the average demand amount and the deviation is calculated. The demand is estimated and estimated.
As a method for performing such prediction, it is common to make predictions on the day using past power demand data.

JP 2009-251742 A

  However, when the DR control is activated, the power consumption during that time is forcibly reduced, and thus a value far from the original power demand. In that case, it is not desirable to use the measured power value when the DR control is activated as it is for the subsequent prediction.

  The present invention has been made in view of such circumstances, and an object of the present invention is to provide a prediction data management apparatus and a prediction data management method that can utilize information on power consumption even when demand response control is activated. Is to provide.

In order to solve the above-described problem, the present invention provides a prediction used for prediction of power demand for an operation management apparatus that predicts power demand based on an actual power measurement value that is an actual power measurement value used in a facility. A prediction data management device for supplying data for use, wherein an input unit for inputting a read request for prediction data from the operation management device together with designation of a target period used for prediction of power demand, and demand response control were performed A storage unit that stores the measured power value and DR activation information indicating whether or not demand response control is performed in a predetermined period including a past date and time, and DR activation information stored in the storage unit A determination unit that determines whether or not the measured power value at the date and time within the target period is a value at the date and time when demand response control is activated; Based on the determination result of the determination unit, when the actual power value at the date and time of the target period is the actual power value during the time when demand response control is not performed, the actual power value is used as prediction data. When the actual power measurement value at the date and time of the target period is the actual power measurement value at the time when the demand response control is performed, the power used when the demand response control is not performed at the date and time An output unit that outputs the calculated value as prediction data to the operation management device;
It is characterized by having.

In the above-described invention, as the prediction data, for example, in the storage unit, the actual measured power value and the predicted power used in the facility in a predetermined period including the past date and time when demand response control was performed The demand forecast value and the DR activation information indicating whether or not demand response control is performed are stored for each time, and the correction coefficient calculation unit is a date and time included in the target period among the date and time stored in the storage unit. The DR correction coefficient, which is the ratio of the demand forecast value and the actual power measurement value at the date and time when demand response control is performed, is calculated for each date and time when demand response control is performed, and multiple days when demand response control is performed A DR correction coefficient average value, which is an average value of the DR correction coefficients for the same time, is calculated, and an actual value correction unit calculates the calculated DR correction coefficient average value as the demand level. By multiplying the power measured value of the date and time that Pons control is performed to correct the power measured values. The output unit outputs the actual power value corrected by the actual value correction unit to the operation management device when the actual power value at the date and time of the target period is the actual power value at the time when the demand response control is performed. can do.
In addition, as the prediction data, the DR correction coefficient average value and the power demand estimation value are calculated when the above-described average value is obtained, but may be calculated by a weighted average.
In addition, the corrected actual power value may be obtained by correcting the actual power value by another method.
In addition, as the prediction data, for example, the storage unit is a predicted value of the measured power value and the power used in the facility in a predetermined period including the past date and time when demand response control was performed. The demand forecast value and the DR activation information indicating whether or not demand response control is performed are stored for each time, and the output unit displays the actual power measurement value at the date and time of the target period at the time when the demand response control is performed. In the case of the actual power measurement value, the demand prediction value stored in association with the actual power measurement value can be output to the operation management apparatus as prediction data.

  Moreover, the present invention is characterized in that, in the above-described prediction data management apparatus, the output unit outputs prediction data in accordance with a timing at which power demand is predicted based on the actual power measurement value.

  Further, the present invention is the above-described prediction data management apparatus, wherein the storage unit calculates a demand prediction value, which is a value obtained by predicting power used at a date and time in the target period, as the measured power value and the DR activation information. And when the measured power value at the date and time of the target period is the measured power value at the time when the demand response control is performed, the output unit predicts based on the predicted demand value stored in the storage unit. It is characterized by outputting business data.

  Further, the present invention is the above-described prediction data management device, wherein the output unit is configured to store the storage unit when the measured power value at the date and time of the target period is the measured power value at the time when the demand response control is performed. The data for prediction based on the demand forecast value memorize | stored in this and the electric power measured value are output, It is characterized by the above-mentioned.

  In addition, the present invention uses the prediction data management apparatus for predicting the power demand with respect to the operation management apparatus that predicts the power demand based on the actual power measurement value that is the actual power measurement value used in the facility. In the prediction data management method in the prediction data management device that supplies the prediction data, the input unit inputs a read request for the prediction data from the operation management device together with designation of a target period used for prediction of power demand, A determination unit stores, in a predetermined period including a past date and time when the demand response control is performed, the measured power value and DR activation information indicating whether or not the demand response control is performed for each time. With reference to the stored DR activation information, the measured power value at the date and time within the target period is the value at the date and time when demand response control is activated When the output unit is based on the determination result of the determination unit, the actual power measurement value at the date and time of the target period is the actual power measurement value during the time when demand response control is not performed. When the actual power measurement value is output to the operation management apparatus as prediction data, and the actual power measurement value at the date and time of the target period is the actual power measurement value at the time when the demand response control is performed, the demand response control is performed at the date and time. A value calculated for the electric power used when the operation is not performed is output as prediction data to the operation management device.

As described above, according to the present invention, it is determined whether or not the measured power value at the date and time within the target period is the value at the date and time when the demand response control is activated, and the measured power value at the date and time of the target period is determined. When the measured power value during the time when the demand response control is not performed, the measured power value is output to the operation management device as prediction data, and when the measured power value is during the time when the demand response control is performed. Is configured to output the value calculated for the power used when the demand response control is not performed at the date and time as prediction data to the operation management apparatus.
This makes it possible to selectively use the actual measured power value or the corrected actual measured power value depending on whether or not the DR control is activated, and use the actual measured power value when the DR control is activated when performing the prediction. It becomes possible to do.

1 is a schematic block diagram showing a configuration of a smart grid system including an operation management device according to an embodiment of the present invention. 4 is a diagram illustrating an example of information stored in a storage unit 41. FIG. It is a flowchart explaining the operation | movement which manages the data for prediction of the operation management apparatus 4. FIG. It is a figure explaining the relationship between a demand predicted value and an electric power actual measurement value.

Hereinafter, an operation management apparatus according to an embodiment of the present invention will be described with reference to the drawings.
FIG. 1 is a schematic block diagram showing a configuration of a smart grid system including an operation management apparatus according to an embodiment of the present invention.
The smart grid system is a combination of a distributed power source such as photovoltaic power generation and a centralized power source from an electric power company, and is constructed to efficiently supply power by adjusting the balance between supply and demand in real time. It is a power receiving system.
The smart grid system shown in FIG. 1 is a facility owned by a power consumer. In addition, it is assumed that the customer of the smart grid system shown in the figure has a demand contract in order to purchase power from an electric power company. In other words, the demand power used in the facility or the like per a measurement period (demand time limit, for example, 30 minutes) by a predetermined unit time is measured, and the contract power (in accordance with the largest demand power among the measured demand powers) Demand target value) and a charge corresponding to this.
For this reason, the consumer needs to make sure that the purchased power for each demand period does not exceed the demand target value. The smart grid system of the present embodiment includes a demand response control (DR control) function for controlling the load and the power supply equipment so that the purchased power for each demand period does not exceed the demand target value.

In this figure, the smart grid system includes an air conditioning heat source facility 1, a work facility 2, a power supply facility 3, an operation management device 4, a load output control device 5, and a power supply output control device 6. The air conditioning heat source facility 1 and the work facility 2 are loads that operate and function by receiving power supplied from the power supply facility 3. The air conditioning heat source facility 1 and the work facility 2 are hereinafter also referred to as load facilities.
The operation management device 4, the load output control device 5, and the power output control device 6 are devices that realize functions corresponding to DR control.

The air-conditioning heat source facility 1 has an air-conditioning heat source unit, an air-conditioning system, a heat circulation mechanism, and the like. The medium is heated and cooled by the air-conditioning heat source unit, and the medium is supplied to the air-conditioning system by a heat circulation mechanism composed of pipes and the like. The air conditioning system cools and heats the space to be supplied and air-conditioned.
The work facility 2 is various work devices provided in the facility. Examples of the work equipment include a computer, a lighting equipment, an OA (Office Automation) equipment, and the like.
The power supply facility 3 includes a generator, a storage battery, a purchased power supply, and the like, and supplies power generated by the power generation, stored power, purchased power, and the like to the air conditioning heat source facility 1 and the work facility 2 that are loads. Moreover, the power supply equipment 3 accumulate | stores generated electric power and purchased electric power in a storage battery in the state with a margin in supply power.

  The operation management device 4 performs DR control to predict power demand based on an actual power measurement value that is an actual power measurement value used in a facility (for example, a building), and the purchased power is a demand target value. The operation of the load equipment (air conditioning heat source equipment 1, work equipment 2, etc.) and the power supply equipment 3 is managed so as not to exceed.

The storage unit 41 stores various types of prediction data used for performing prediction. For example, in the storage unit 41, whether the actual power measurement value and the demand prediction value that is a predicted value of the power used in the facility and the DR control have been performed in a predetermined period including the past date and time when the DR control was performed. The DR activation information indicating whether or not is stored for each time.
The storage unit 41 also stores weather forecast data, power load pattern data, and the like as prediction data.

The prediction data management unit 42 includes a determination unit 421 and a correction unit 422. Upon receiving a request for reading prediction data from the prediction unit 43 along with designation of a target period used for power demand prediction, the prediction data management unit 42 responds to this request. Thus, the prediction data is supplied from the storage unit 41 to the prediction unit 43. The prediction data is data that is necessary for the prediction unit 43 to calculate a power prediction value that is a predicted value of power consumption on the prediction target day. The power measurement value and the prediction data management unit 42 The power demand estimation value, which is a corrected actual power value, weather forecast data, power load pattern data, and the like are included.
Further, the prediction data management unit 42 stores the demand prediction value predicted by the prediction unit 43 in the storage unit 41. The demand prediction value is a prediction value of power demand for each date and time of the prediction target, which is a result calculated by the prediction unit 43 predicting.

  The determination unit 421 refers to the DR activation information stored in the storage unit 41 and determines whether or not the actually measured power value at the date and time within the target period is the value at the date and time when the DR control is activated.

  The correction unit 422 includes a correction coefficient calculation unit 423 and an actual measurement value correction unit 424, and corrects the actual power measurement value at the date and time when DR control was performed according to the determination result of the determination unit 421. Then, an estimated power demand value is calculated.

  The correction coefficient calculation unit 423 is the past predicted date and time stored in the storage unit 41, and the demand predicted value and power at the date and time when DR control is performed on the predicted use date (target period) that is the date used for power demand prediction. A DR correction coefficient that is a ratio to the actual measurement value is calculated for each date and time when DR control is performed, and a DR correction coefficient average value that is an average value of DR correction coefficients at the same time on a plurality of days on which DR control is performed. calculate.

Here, the correction coefficient calculation unit 423 calculates the DR correction coefficient x _ij according to the following equation (1). Here, x _ij is a DR correction coefficient at j time on j day. i is a certain time of the day, for example, any one of the days divided by a fixed time period (for example, demand time period), more specifically, from 0:00 Any one of the times obtained by dividing 23:59 by demand time (for example, in units of 30 minutes) can be used. j is a date included in the target period to be used as the predicted use date among the past days, and is the date when the DR control is activated. PF _ij is a demand forecast value at i time of j day. PA _ij is a power actual measurement value at i time on j day. The DR correction coefficient x _ij represents the DR correction coefficient for j day i day.

Further, the correction coefficient calculation unit 423 calculates the DR correction coefficient average value X _i according to the following equation (2). X _i is the DR correction coefficient average value at i time. This DR correction coefficient average value X _i is the sum of the DR correction coefficients x _ij at i time on each day when DR control is activated from the first day to the last day used for obtaining the DR correction coefficient average value. It is determined by dividing by D _DR. Here, the date used for obtaining the DR correction coefficient average value may be different from the predicted use date. For example, the most recent one week can be used as the predicted use date, and the past one month can be used as the date used to obtain the DR correction coefficient average value. _DDR is the number of days in which DR is activated in the period to be used as the prediction data, that is, the number of days in the past that is included in the period of the target to be used for prediction and in which DR control is activated. is there.

  The actual measurement value correcting unit 424 multiplies the calculated actual value of the DR correction coefficient by the actual power measurement value at the date and time when the DR control is performed to correct the actual power measurement value at the time of the prediction target day, and the corrected actual power measurement value. Is calculated as an average value in the number of days included in the target period used for prediction among the past days and the DR control is activated, thereby calculating the power demand estimated value. The actual measurement value correcting unit 424 corrects the actual power measurement value and calculates the estimated power demand value every time when the DR control is performed.

The actual value correction unit 424 calculates the power demand estimated value P _{ik according} to the following equation (3). The power demand estimated value P _ik is a power demand estimated value at i time of k days. k is the date of the prediction date. P _ij is an actual power demand value at j time on i. The j day in P _ij may not be the same day as the j day in PA _ij . D _FC is the number of days of the date of use to predict. This is equal to the total number of days of j described above.

The prediction unit 43 is a predicted power value that is a value obtained by predicting the power consumed by various devices provided in the load facility (air-conditioning heat source facility 1 and work facility 2) at regular intervals in a certain future period. Is calculated based on various data stored in the storage unit 41 and prediction data obtained from the prediction data management unit. For example, the prediction unit 43 calculates a predicted value for each fixed time period (for example, demand time period) in a certain period in the future (for example, one day (24 hours)).
As this predicted value, for example, an air conditioning heat load predicted value, which is the amount of heat required by the air conditioning heat source unit for each fixed time period (for example, demand time period) in a future fixed period (for example, one day (24 hours)), is used. Predict based on air conditioning heat load prediction data. The air-conditioning heat load prediction data includes, for example, weather forecast data, load power pattern data, actual power measurement values, and the like. As the actual measured power value, the actual measured power value, the estimated power demand value, the weather forecast data, and the power load pattern data supplied from the prediction data management unit 42 are used. The load power pattern data is a pattern according to the passage of time with respect to the power consumption of each device in the load facility (air conditioning heat source facility 1 or work facility 2). There are various load power patterns such as the equipment to be used (for example, a room), the number of people using the equipment, the load used there (computer, lighting, OA equipment), air conditioning, season, temperature of the day, humidity, sunlight, etc. It depends on the situation.
Further, the prediction unit 43 uses an air conditioning system device power consumption prediction value, which is the power consumed by each air conditioning system device forming the air conditioning system at regular intervals in a certain future period, for air conditioning system device power consumption prediction. Predict based on data.
In addition, the prediction unit 43 predicts a generated power predicted value, which is power output from the power generation apparatus, at a predetermined interval in a future fixed period based on the generated power prediction data.

The operation plan creation unit 44 includes a prediction unit 43, and is stored in the storage unit 41, the air conditioning thermal load prediction value predicted by the prediction unit 43, the air conditioning system device power consumption prediction value, and the generated power generation prediction value. Based on the load power pattern data, operation plan data indicating an operation plan for one day is created by at least a day before one day. As the operation plan, the operation plan creation unit 44 can execute a dedicated program for optimization and create an optimum air conditioning heat source operation plan and power supply facility operation plan.
The operation plan creation unit 44 creates an air conditioning heat source machine operation plan, an air conditioning system equipment operation plan, and a power supply facility operation plan as operation plans.
In the air conditioning heat source unit operation plan, the air conditioning heat load and the added heat amount (the sum of the production heat amount produced by the air conditioning heat source unit and the heat radiation amount radiated from the heat storage tank) are equal at regular time intervals (demand time period) in one day. It is the information showing the operation state of an air-conditioning heat-source machine.
The air conditioning system equipment operation plan is based on the predicted time of power consumption, and for each external air conditioner, air conditioner, and packaged air conditioner, considers the stop period and the preparatory operation period immediately before it. Is information. The preparatory operation period is a period in which precooling operation or preheating operation is performed so that comfort is not lost during the stop period when the external air conditioner, air conditioner, package air conditioner, etc. are temporarily stopped to reduce power consumption. .
The power supply facility operation plan includes the first generator, the second generator, the storage battery, and the purchased power supply for supplying power to the load facilities (air conditioning heat source facility 1 and work facility 2) at regular intervals in the day. It is the information showing the breakdown of allocation of the electric energy output from each.
The operation plan creation unit 44 generates the air conditioning heat source machine operation plan, the air conditioning system equipment operation plan, and the power supply facility operation plan, for example, by computer processing according to an algorithm of a mathematical programming method.

The load output control device 5 controls the output of the air-conditioning heat source unit 11 at regular intervals based on the air-conditioning heat source unit operation plan created by the operation management device 4, and at regular intervals based on the air-conditioning system equipment operation plan. Controls the output of the air conditioning system for each time period.
Based on the air conditioning heat source unit operation plan created by the operation management device 4, the power output control device 6 has a first generator 31, a second generator 32, a storage battery 33, and a purchased power source 34 at regular intervals. To control the power supplied to the load equipment (air conditioning heat source equipment 1, work equipment 2).

  As described above, the operation state of the air conditioning heat source unit 11 and the air conditioning system 12 is controlled according to the operation plan (air conditioning heat source unit operation plan, air conditioning system equipment operation plan, etc.), and output from each power source in the power source facility 3. Control power. Thus, DR control is implemented so that the power output from the power purchase power supply 34 (power purchase power) does not exceed the demand target value.

  FIG. 2 is an example of information stored in the storage unit 41. In particular, among the information stored in the storage unit 41, the DR activation information (corresponding to the DR activation flag), the actual power measurement value, and the demand prediction value are The information memorize | stored for every date and time is illustrated. Moreover, in this figure, the DR correction coefficient average value and the power demand estimated value are further stored in association with these pieces of information. In addition, “−” representing that no information is stored is shown on the date and time when the DR correction coefficient average value and the power demand estimated value are not calculated.

The DR activation information is information indicating whether DR control is activated. The DR activation information is represented by a flag, for example, indicating that “1” has been activated, and that “0” has not been activated.
The date is the date when the actual power measurement value is measured, and the time represents the measured time.

For example, at 11 o'clock on August 3, DR control is activated, the demand prediction value is a1 (kW), the actual power measurement value is b1 (kW), the DR correction coefficient average value is c1, the power demand estimation value ( The value after correcting the actual power measurement value) is d1 (kW).
At 18:00 on August 5, the DR control is not activated, the demand prediction value is a8 (kW), the actual power measurement value is b8 (kW), the DR correction coefficient average value is c1, and the power demand estimation value is d1 ( kW).

Next, the operation of the operation management device 4 will be described. FIG. 3 is a flowchart for explaining the operation of managing the prediction data of the operation management device 4.
First, the determination unit 421 of the prediction data management unit 42 designates a target period used for prediction of power demand from the prediction unit 43, and when there is a request for obtaining prediction data (step S10), the designated period. The power actual measurement value corresponding to the date and time included in is read from the storage unit 41 (step S11). Then, the determination unit 421 determines whether or not the DR activation information associated with each read actual power value indicates that the DR control has been activated (step S13).
If the DR activation information does not indicate that DR control has been activated, the actual power measurement value associated with the DR activation information is output to the prediction unit 43 as prediction data (step S18).
On the other hand, when the DR activation information indicates that DR control has been activated, the correction unit 422 determines whether or not the DR correction coefficient average value is stored in association with the actual power measurement value (step S14). ).

  When the DR correction coefficient average value is stored in association with each other, the correction unit 422 calculates the power demand estimated value by multiplying the actual power measurement value by the DR correction coefficient average value by the actual value correction unit 424 (step S42). S17), the calculated power demand estimation value is supplied as prediction data to the prediction unit 43 (step S18).

  On the other hand, when the average power correction value is not associated with the average value of the DR correction coefficient, the correction unit 422 calculates the DR correction coefficient by the correction coefficient calculation unit 423 (step S15), and the DR correction coefficient at the same time. Is calculated, the DR correction coefficient average value is calculated and stored in the storage unit 41 in association with the corresponding actual power measurement value (step S16). Then, the correction unit 422 calculates the power demand estimated value by multiplying the actual power value measured by the actual value correction unit 424 by the obtained DR correction coefficient average value (step S17), and the calculated power demand The estimated value is supplied to the prediction unit 43 as prediction data (step S18).

  Next, further description will be given with reference to FIG. When the target period used for prediction of power demand is designated as 3:00 on August 3rd to 23:00 on August 4th and there is a request for obtaining prediction data, the correction unit 422 stores information stored in the storage unit 41. , The actual measured power value is read out, and it is determined whether or not the actual measured power value is the date and time when the DR control is activated. For example, the actual power measurement value b5 at 18:00 on August 4 is an actual power measurement value during the time when DR control is not performed, and thus is supplied to the prediction unit 43 as prediction data. Since power measurement values b1, b2, b3, and b4 on August 3, 11:00, 12:00, August 4, 11:00, and 12:00 are power measurement values during the time when DR control is activated, After the correction, it is supplied to the prediction unit 43.

  When calculating the DR correction coefficient, the correction coefficient calculation unit 423 calculates a DR correction coefficient that is a ratio (a1 / b1) between the demand prediction value a1 and the actual power measurement value b1 at 11:00 on August 3, and A DR correction coefficient which is a ratio (a3 / b3) between the demand forecast value a3 and the actual power measurement value b3 at 11 o'clock on August 4, which is within the reading target period and the same time, is calculated, and this DR correction coefficient The average value (((a1 / b1) + (a3 / b3)) / 2) is calculated, and the DR correction coefficient average value C1 that is the calculation result is obtained. Then, the correction coefficient calculation unit 423 associates the obtained DR correction coefficient average value c1 with the actual power measurement value as the DR correction coefficient average value on August 3 at 11:00 and on August 4 at 11:00. To remember.

The correction coefficient calculation unit 423 calculates a DR correction coefficient that is a ratio (a2 / b2) between the demand forecast value a2 and the actual power measurement value b2 at 12:00 on August 4, and is within the target period of reading. A DR correction coefficient that is a ratio (a4 / b4) of the demand predicted value a4 and the actual power measurement value b4 at 12:00 on August 4 at the same time is calculated, and an average value of the DR correction coefficients (((a2 / b2) + (a4 / b4)) / 2) is calculated, and the DR correction coefficient average value c3 which is the calculation result is obtained. Then, the correction coefficient calculation unit 423 associates the obtained DR correction coefficient average value c3 with the actual power measurement value as the DR correction coefficient average value on August 3 at 12:00 and August 4 at 12:00. To remember.
In this way, the correction coefficient calculation unit 423 calculates the DR correction coefficient average value at the date and time when the DR control is performed, and stores it in the storage unit 41. Here, the average value of the DR correction coefficient at 11:00 and 12:00 was calculated. However, for example, DR control is also performed at 13:00 on August 3rd and 4th, and at 14:00 on August 3rd and 4th. In the case where it is detected, the DR correction coefficient average value is similarly calculated and stored in the storage unit 41.

The actual measurement value correcting unit 424 calculates the corrected actual power measurement value by multiplying the actual power measurement value b1 at 11 o'clock on August 3 at which DR control is performed by the DR correction coefficient average value c1, and calculates the corrected power actual measurement value. For the day 11:00, the corrected power measurement value is calculated by multiplying the power measurement value b3 by the DR correction coefficient average value c1. Then, the actual measurement value correction unit 424 calculates the average value of the corrected power actual measurement value at 11:00 on August 3 and the corrected power actual measurement value at 11:00 on August 4 to thereby determine the August 3rd. The power demand estimated value d1 at 11:00 on August 4 is calculated, stored in the storage unit 41, and supplied to the prediction unit 43 as prediction data. In addition, the actual measurement value correction unit 424 calculates a power actual measurement value corrected by multiplying the power actual measurement value b2 by the DR correction coefficient average value c3 for August 3 at 12:00. By calculating the actual measured value corrected by multiplying the actual measured value b4 by the DR correction coefficient average value c3, and obtaining these average values, the estimated power demand d2 at 12 o'clock from August 3 to August 4 is obtained. Is stored in the storage unit 41 and supplied to the prediction unit 43 as prediction data.
In the case of performing the next prediction, the DR correction coefficient average value and the power demand estimated value stored in the storage unit 41 are deleted and newly calculated according to a new predicted use date.

FIG. 4 is a diagram illustrating the relationship between the demand forecast value and the actual power measurement value. In this figure, the horizontal axis represents time, and the vertical axis represents power. Here, the demand forecast value and the actual power measurement value are represented for each time. Further, a part of the graph is enlarged and shown in an area indicated by reference numeral 100. For example, the demand forecast value (symbol A) at 13:00 exceeds the contract power (demand target value) (symbol C), and as a result of the DR control being activated, the actual power measurement value (symbol B) is the demand target value. Can be below. In this case, the actual power measurement value (symbol B) is multiplied by the DR correction coefficient average value (x ₁₃ ), and the actual power measurement value at the same time on consecutive days before and after this day is multiplied by the DR correction coefficient average value. By calculating the number and the average value, it can be used as prediction data.

  In the embodiment described above, the configuration in which the operation management device 4 includes the prediction data management unit 42 has been described. However, the prediction data management unit 42 is provided outside the operation management device 4 and is an independent device (prediction data management device). Also good. In this case, the operation management device 4 transmits a prediction data read request to the prediction data management device, and the prediction data management device receives this request and performs the same processing as described above, so that the operation management device 4 stores the request. The prediction data may be read by accessing the unit 41. In this case, the storage unit 41 may be provided outside the operation management device 4 or the prediction data management device, or may be provided inside the prediction data management device. In this case, the prediction data management device is provided with an input unit for inputting information from the operation management device 4 and an output unit for outputting information to the operation management device 4.

  In the embodiment described above, when the DR control is activated, the actual power measurement value is different depending on whether the DR control is activated or not when the DR control is activated. That is, when DR control is not activated, power of 1400 kW, for example, is consumed. However, when DR control is activated, power consumption is suppressed, so that the power may be reduced to, for example, about 1100 kW. Then, since the predicted value of power consumption is generally calculated in consideration of the relationship between the actual measured power value in the past and the climate such as the temperature, humidity, and wind direction at that time, the number of people who are active in the facility, etc., the DR control Since the power consumption is forcibly suppressed by triggering, the relationship such as temperature, humidity, wind direction, etc. at that time and the number of people who are active in the facility will be reduced, and DR control will not be triggered. In this case, it becomes a value far from the actual power measurement value that would have been consumed, and cannot be used as future prediction data.

When power demand prediction is performed, if possible, it is desired that there is a large amount of data that can be used as prediction data. Therefore, there is a desire to use an actual power measurement value at the date and time when DR control is activated. However, if the actual power measurement value at this time cannot be used due to the activation of the DR control, data in a period in which the DR control is not activated is used. Then, for example, even when it is desired to use data from 1 day to 3 days before the target date for prediction, if the data for 2 weeks before is used as a period when DR control is not activated, the demand changes. (For example, a new computer has recently been installed, or personnel changes have affected the equipment load due to changes in personnel placement or changes in work content) You may want to use it.
Therefore, it is desired to perform prediction reflecting the environment (state, weather condition, etc.) as close as possible to the prediction target date by using the latest data as much as possible.

  Therefore, according to the operation management device 4 to which the above-described prediction data management unit 420 is applied, by calculating and multiplying the actual measured power value at the date and time when the DR control is activated, by calculating and multiplying the DR correction coefficient average value, at that date and time. It is possible to estimate an actual power measurement value when DR control is not activated, and this can be used for prediction.

  In the above-described embodiment, the DR correction coefficient average value and the power demand estimated value are calculated by simple averaging. However, the power demand estimated value may be obtained by calculating by weighted averaging. Moreover, you may make it output the value obtained by calculating to the driving | operation management apparatus 4 as prediction data by correct | amending an electric power measurement value by methods other than the above. In addition, as the prediction data, for example, a demand prediction value stored in the storage unit can be used.

  In addition, according to the above-described embodiment, it is possible to estimate the measured power value without measuring the power of the device that is the target of the DR control, so the cost of installing the measuring device that measures the power of the device can be reduced. Can be suppressed.

  In addition, according to the above-described embodiment, it is possible to estimate a measured power value in consideration of a reduction in power consumption of a device that is manually turned off when the DR control is activated.

  In addition, a program for realizing the function of the operation management device 4 in FIG. 1 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 manage the operation. Or forecast data management may be performed. Here, the “computer system” 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. Further, the “computer-readable recording medium” refers to a volatile memory (RAM) in a computer system that becomes a server or a client when a program is transmitted via a network such as the Internet or a communication line such as a telephone line. In addition, those holding programs for a certain period of time are also included. The program may be a program for realizing a part of the functions described above, and may be a program capable of realizing the functions described above in combination with a program already recorded in a computer system.

  The embodiment of the present invention has been described in detail with reference to the drawings. However, the specific configuration is not limited to this embodiment, and includes designs and the like that do not depart from the gist of the present invention.

DESCRIPTION OF SYMBOLS 1 Air-conditioning heat source equipment 2 Work equipment 3 Power supply equipment 4 Operation management apparatus 5 Load output control apparatus 6 Power supply output control apparatus 41 Memory | storage part 42 Prediction data management part 43 Prediction part 44 Operation plan preparation part 421 Judgment part 423 Correction coefficient calculation part 424 Actual measurement Value correction unit

Claims (5)

A prediction data management device that supplies prediction data used for prediction of power demand to an operation management device that predicts power demand based on actual power measurement values that are actual power values used in a facility,
An input unit for inputting a read request for prediction data from the operation management device together with designation of a target period used for prediction of power demand;
A storage unit that stores the measured power value and DR activation information indicating whether or not demand response control has been performed in a predetermined period including a past date and time when demand response control is performed;
A determination unit that refers to the DR activation information stored in the storage unit and determines whether or not the measured power value at the date and time within the target period is a value at the date and time when the demand response control is activated;
Based on the determination result of the determination unit, when the measured power value at the date and time of the target period is the measured power value during the time when demand response control is not performed, the measured power value is used as prediction data. When the actual power measurement value at the date and time of the target period is the actual power measurement value at the time when the demand response control is performed, the power used when the demand response control is not performed at the date and time An output unit that outputs the calculated value as prediction data to the operation management device;
The prediction data management apparatus characterized by having.   The prediction data management apparatus according to claim 1, wherein the output unit outputs prediction data in accordance with a timing at which power demand is predicted based on the actual power measurement value. The storage unit stores a predicted demand value, which is a predicted value of power used for the date and time within the target period, together with the actual power measurement value and the DR activation information,
The output unit outputs prediction data based on the demand prediction value stored in the storage unit when the actual power value at the date and time of the target period is the actual power value at the time when the demand response control is performed. The prediction data management apparatus according to claim 1, wherein: The output unit is based on the demand prediction value and the power measurement value stored in the storage unit when the power measurement value at the date and time of the target period is the power measurement value at the time when the demand response control is performed. The prediction data management apparatus according to any one of claims 1 to 3, wherein prediction data is output. Predictive data management in a predictive data management apparatus that supplies prediction data used for prediction of power demand to an operation management apparatus that predicts power demand based on the actual power value that is actually measured power used in a facility A method,
The input unit inputs a request for reading prediction data from the operation management device together with designation of a target period used for power demand prediction,
A determination unit stores, in a predetermined period including a past date and time when the demand response control is performed, the measured power value and DR activation information indicating whether or not the demand response control is performed for each time. With reference to the stored DR activation information, it is determined whether the measured power value at the date and time within the target period is a value at the date and time when demand response control is activated,
Based on the determination result of the determination unit, when the output actual measurement value at the date and time of the target period is the actual power measurement value during the time when demand response control is not performed, the actual power measurement value is used as prediction data. Is used when the demand response control is not performed at the date and time when the measured power value at the date and time of the target period is the actual power value at the time when the demand response control is performed. A predicted data management method, wherein a value calculated for the electric power to be output is output to the operation management device as prediction data.

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