JP2014078127A - 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 on the same day of the week as a prediction target date from a measured value storage unit storing it with a date and a day of the week; a statistical processing unit that calculates a statistical value obtained by statistically processing the past power consumption value on the same day of the week or its time dependency; and a power consumption prediction unit that sets the statistical value calculated by the statistical processing unit as a power consumption value on the prediction target date or its time dependency.

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 housing 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 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 past power consumption value of the same day as the prediction target day or a time dependency thereof from a measured value storage unit that stores the date and day of the week together, and a past of the same day of the week Statistical processing unit for obtaining statistical values obtained by statistically processing the power consumption value or time dependency thereof, and a power consumption prediction unit for using the statistical value obtained by the statistical processing unit as a power consumption value for the prediction target day or its time dependency And an information processing apparatus characterized by comprising:
The power consumption reading unit has a time dependency of the first power consumption value of the past x days on the same day before the reference date, for example, the same day of the week before the prediction target date, for example, the same day of the week before the reference date. , The time dependency of the second power consumption value of the y day (y> x) longer (period) than the x day of the same day before the reference date is read from the measured value storage unit, and the statistics The processing unit includes a first statistical value obtained by statistically processing the first power consumption or its time dependency, and a second statistical value obtained by statistically processing the second power consumption or its time dependency, And a coefficient calculation unit that calculates a weighting coefficient between the first statistical value and the second statistical value, and the power consumption prediction unit is calculated by the statistical processing unit based on the weighting coefficient The statistical value is assumed to be the power consumption value of the forecast target day or its time dependency. To. Thereby, period dependence can be considered in dependence on day of the week.

It is preferable that the statistical value not depending on the day of the week is obtained in the period immediately before the prediction target day, and the statistical value depending on the day of the week is obtained on the same day of the week before the prediction target day. It is recommended that the required period be different depending on the presence or absence of dependence.
The power consumption reading unit further sets a past power consumption value of the same day before the reference date and a past power consumption before the reference date, with the prediction target date and the same day of the week before as a reference day. The statistical processing unit statistically processed the first statistical power value or the time dependency thereof and the second statistical power value or the time dependency thereof statistically. And a coefficient calculation unit that calculates a weighting coefficient between the first statistical value and the second statistical value, and the power consumption prediction unit is based on the weighting coefficient. It is preferable that the statistical value obtained by the statistical processing unit be the power consumption value of the prediction target day or its time dependency.

The statistical value may be an average value, and the statistical value may be an extrapolated value.
The present invention may be a device including any of the information processing apparatuses described above, and is determined in advance, for example, so as to be a reference to the power consumption prediction value predicted by the information processing apparatus described above. It is also possible to perform control to reduce the power consumption value if the predicted result exceeds the reference value based on the comparison result.
Further, the present invention may be 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 past power consumption value of the same day as the prediction target day or a time dependency thereof from a measured value storage unit that stores the date and day of the week, and the same day of the week A statistical processing step for obtaining a statistical value obtained by statistically processing a past power consumption value or its time dependency, and a power consumption prediction using the statistical value obtained by the statistical processing step as a power consumption value for the prediction target day or its time dependency And an information processing method comprising the steps. 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 functional block diagram which shows the example of 1 structure of the information processing part which performs the process by the power consumption prediction program by the 2nd Embodiment of this invention. It is a figure which shows the prediction target day of power consumption, 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 figure which shows the example (Here, the example which calculates | requires time dependence is shown.) When it is set as a statistical value by an extrapolated value instead of the average value shown in FIG. It is a flowchart figure which shows the flow of a process in the information processing part by the 2nd 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 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 an information processing technique related to power consumption prediction in which attention is paid to the fact that the power consumption of devices varies greatly depending on the user's lifestyle, and in particular, the dependence on day of the week is large.

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. The information processing apparatus may be connected to the air conditioner apparatus in a wired or wireless manner. 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 of the device and temperature control from the remote control device 17 is received by the remote control signal light receiving unit 31. 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 of the device measured by the power consumption measuring instrument 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. 2A 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 unit 21a includes a power consumption reading unit 21a-1 that reads power consumption values from a memory, a statistical processing unit 21a-2 that performs statistical processing related to information processing of power consumption values, and a power consumption value that has undergone statistical processing And a power consumption prediction unit 21a-3 that predicts a power consumption value based on the above.
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.

When the information processing is started (step S1), the power consumption reading unit 21a-1 predicts the prediction target date (July 28th) 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 value of the same day (Saturday of the calendar starting from Monday in FIG. 3) is read from the measured value storage unit 27a (S2). For example, the power consumption values on July 21st, July 14th, and July 7th are read out on Saturday of July.
The statistical processing unit 21a-2 obtains a statistical value (for example, an average value) obtained by statistically processing the past power consumption value of the same day (step S3). The power consumption prediction unit 21a-3 obtains the power consumption value on the prediction target day based on the statistical value obtained by the statistical processing unit 21a-2 (step S4). If it is an average value, the power consumption value on July 28, which is the prediction target day, is obtained by calculating the average over three days.

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 S11), the power consumption reading unit 21a-1 predicts the prediction target date (July 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 day of the week. 28), the time dependence of the past power consumption value on the same day of the week is read out from the measured value storage unit (step S12). For example, the time dependence of the power consumption values on July 21, July 14, and July 7 is read.

  The statistical processing unit 21a-2 obtains a statistical value (average value) obtained by statistically processing the time dependence of the past power consumption value of the same day (step S13). The power consumption prediction unit 21a-3 obtains the time dependency of the power consumption value on the prediction target day based on the statistical value obtained by the statistical processing unit 21a-2 (step S14). Here, the average value over three days is obtained as the average value, thereby making the power consumption value of July 28, which is the prediction target day, time-dependent, and the process is terminated (step S15).

FIG. 6 is a diagram illustrating an example in which an extrapolated value is a statistical value instead of the average value illustrated in FIGS. 4 and 5 (here, an example in which time dependence is obtained) is illustrated.
When the information processing is started (step S21), the power consumption reading unit 21a-1 predicts the prediction target day (July 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 day of the week. 28), the time dependence of the past power consumption value on the same day of the week is read out from the measured value storage unit (step S22). For example, the power consumption values on July 21, July 14, and July 7 are read out.

  The statistical processing unit 21a-2 obtains a statistical value (extrapolated value) obtained by statistically processing the time dependence of the past power consumption value of the same day (step S23). The extrapolated value is an extrapolated value to the prediction target date. 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 values that shorten the period (for example, the period between the prediction target days is 0). The values of July 28, July 14, July 21, and the power consumption values on the time axis are plotted to obtain the value of July 28. The power consumption prediction unit 21a-3 obtains the time dependency of the power consumption value on the prediction target day based on the statistical value obtained by the statistical processing unit 21a-2 (step S24). Here, the extrapolated value is the power consumption value of July 28, which is the prediction target date, or its time dependence, and the process ends (step S25).

With any of the above processes, the power consumption depending on the day of the week for the usage status of the user's air conditioner device can be obtained.
In this way, it is considered that the day of the week is the most likely to affect the lifestyle that affects power consumption, such as at home and away from home, so power prediction that depends on the day of the week can be used to accurately predict power consumption. Can be done well.

Next, an information processing technique according to the second embodiment of the present invention will be described. The basic configuration is the same as in FIG. FIG. 3 is also used for explanation.
FIG. 2B is a functional block diagram illustrating a configuration example of the information processing unit 21b that performs processing by the power consumption prediction program 27c according to the present embodiment. FIG. 7 is a flowchart showing the flow of processing in the information processing section 21b.
The information processing unit 21b includes a power consumption reading unit 21a-1 that reads 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. A coefficient calculation unit 21b-1 that obtains a weighting coefficient of the dependency based on the value, and a power consumption prediction unit 21a- that predicts the power consumption value based on the power consumption value as a result of performing statistical processing and coefficient calculation 3.
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.

When the information processing is started (Start: Step S51), the power consumption reading unit 21a-1 predicts the prediction target date (July 28) from the measurement 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 value of the same day of the week is read out (step S52). For example, with the prediction target date (July 28 (Saturday)) and the same day one week ago (July 21 (Saturday)) as the reference date, the past power consumption value of the same day before this reference date and The past power consumption value (for example, the period from July 1 to July 20) before the reference date (Saturday, July 21) is read from the measured value storage unit 27a. The past power consumption value before the reference date may be excluded on the same day (Saturday) in order to clarify whether or not it depends on the day of the week.
The statistical processing unit 21a-2 includes a first statistical value (for example, an average value) obtained by statistically processing past power consumption values on the same day, and a second statistical value (for example, an average value) obtained by statistically processing past power consumption values. ) Is obtained (step S53). The coefficient calculation unit 21b-1 obtains a weighting coefficient between the first statistical value and the second statistical value (step S54). Then, the power consumption prediction unit 21a-3 obtains the power consumption value on the prediction target day based on the weighting coefficient (step S55). Then, the process ends (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 all past periods (for example, July 1st to 20th) (independent of day of week)
B (t): Average power consumption value of past designated days (for example, July 7th and 14th) (depends on day of week)
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)
The combination of the coefficients (a, b) 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 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. 8, 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.
As the statistical value, an average value may be used, and an extrapolated value may be used 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. 1 and 2B. FIG. 3 is also used for explanation.
FIG. 9 is a flowchart showing the flow of processing in the information processing section 21b according to this embodiment.
The process is started (step S71), from the measured value storage unit 27a that stores the daily power consumption together with the date and day of the week, the power consumption reading unit 21a-1 is based on the same day one week before the prediction target day. The first power consumption value of the past x days of the same day prior to the reference date and the y day (y> x) longer than the past x days of the same day prior to the reference date. The second power consumption value is read (step S72). Next, 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 obtained by statistically processing the second power consumption value. (Step S73). Next, in step S74, the coefficient calculation unit 21b-1 obtains a weighting coefficient between the first statistical value and the second statistical value. In step S75, the power consumption prediction unit 21a-3 obtains a power consumption value on the prediction target day based on the weighting coefficient (end in step S76).

Constants b and c for obtaining the prediction result are obtained. In the calendar example, data up to the 20th is used and compared with the 21st.
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).
B (t): Average power consumption value on the first designated day in the past (for example, July 7 and July 14)
C (t): Average power consumption value in the second designated day in the past (for example, June 23, June 30, July 7, and July 14)
D (t): Power consumption value on the target day (the day one week before the forecast date) for fitting the forecast results
D (t) = bB (t) + cC (t) (3)
Calculate the combination of b and c that is closest. Here, t is time.
The calculated coefficient b, c and the B ′ (t) and C ′ (t) for the prediction target date (Saturday, July 28), the power consumption value D ′ (t ) Is calculated.
D '(t) = bB' (t) + cC '(t) (4)
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. 10, the time dependency of the power consumption value can be obtained instead of the power consumption value (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 in FIGS. 1 and 2B. FIG. 3 is also used for explanation. FIG. 11 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. 11, in step S201, a predicted value of power consumption and a budget (such as the 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 (it is set as control which makes power consumption 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. 12 is a functional block diagram showing a configuration example of the HEMS according to the present embodiment.
As shown in FIG. 12, the power consumption from each home appliance A to D is measured by the “outlet plug type power sensor” 18 or the “CT sensor” 18a attached to the 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 (21a-3) and a scheduler unit 33. The home appliance power consumption prediction unit 31 has a configuration corresponding to the power consumption prediction unit 21a-3 of FIG. Or the structure which acquires collectively the prediction information from the power consumption estimation part 21a-3 of FIG. 2 and utilizes for control may be sufficient.
Further, FIG. 12 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 consumption usage history in the home, and predicts future power consumption in the home together with the season / weather and input information of various sensors. .
The home appliance scheduler unit 33 performs scheduling related to the home appliance operation state (level shift = On / Off, temperature, brightness, etc.) and operation timing (time shift, that is, change of use time zone) according to the power consumption prediction value and the like. 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 system 51, the storage battery system 53, and the electric vehicle 55, and also relates to the overall energy consumption of the home appliances AD in the home. Generate plan information for 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 scheduler unit 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, the target date, the period for obtaining the first and second statistical values, the specified day of the week, etc. can be arbitrarily set from the GUI as shown in FIG. You may comprise so that estimated 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 ... power consumption prediction unit, 21b-1 ... coefficient calculation unit, 23 ... RAM (main storage device), 27 ... ROM (nonvolatile memory), 27a ... measurement value storage unit, 27b: Automatic operation program, 27c: Power consumption prediction program.

Claims (12)

A power consumption reading unit that reads a past power consumption value on the same day as the prediction target day or a time dependence thereof from a measured value storage unit that stores the date and day of the week, and
A statistical processing unit for obtaining a statistical value obtained by statistically processing the past power consumption value or its time dependency on the same day;
An information processing apparatus comprising: a power consumption prediction unit that uses a statistical value obtained by the statistical processing unit as a power consumption of a prediction target day or a time dependency thereof. The power consumption reading unit uses the same day of the week prior to the prediction target date as a reference date, the time dependency of the first power consumption value of the past x days of the same day prior to the reference date, and before the reference date The time dependence of the second power consumption value on the y day (y> x) longer than the x day on the same day of the same day, from the measured value storage unit,
The statistical processing unit includes a first statistical value obtained by statistically processing the first power consumption value or its time dependency, and a second statistical value obtained by statistically processing the second power consumption value or its time dependency. With statistics,
Furthermore, it has a coefficient calculation part which calculates | requires the weighting coefficient of a said 1st statistics value and a said 2nd statistics value, The said power consumption estimation part is the statistics value calculated | required by the said statistics process part based on the said weighting coefficient The information processing apparatus according to claim 1, wherein the power consumption value of the prediction target day or the time dependence thereof is set as the information processing apparatus.   3. The information according to claim 2, wherein the statistical value independent of the day of the week is obtained in a period immediately before the prediction target day, and the statistical value dependent on the day of the week is obtained on the same day of the week more than one week before the prediction target day. Processing equipment. The power consumption reading unit further sets a past power consumption value of the same day before the reference date and a past power consumption before the reference date, with the prediction target date and the same day of the week before as a reference day. Read the value
The statistical processing unit includes a first statistical value obtained by statistically processing the first power consumption value or its time dependency, and a second statistical value obtained by statistically processing the second power consumption value or its time dependency. With statistics,
And a coefficient calculation unit for obtaining a weighting coefficient between the first statistical value and the second statistical value,
The said power consumption prediction part makes the statistical value calculated | required by the said statistical process part based on the said weighting coefficient as a power consumption value of the prediction object day, or its time dependence, The Claim 1 characterized by the above-mentioned. Information processing device.   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 for reading from the measured value storage unit that stores the past power consumption value or its time dependency on the same day as the prediction target day together with the date and the day of the week;
A statistical processing step for obtaining a statistical value obtained by statistically processing the past power consumption value of the same day or its time dependence;
An information processing method comprising: a power consumption prediction step in which the statistical value obtained in the statistical processing step is a power consumption value of a prediction target day or a time dependency thereof.   A program for causing a computer to execute the information processing method according to claim 10.   The computer-readable recording medium which records the program of Claim 11.

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