JP2017078925A - Electricity charge information prediction system - Google Patents

Abstract

PROBLEM TO BE SOLVED: To solve a problem, that is to provide an electricity charge information prediction system capable of adjusting a power use amount in accordance with a target electricity charge during a settlement period and the increase of a unit cost during the settlement period.SOLUTION: An electricity charge information predication system includes: use amount measurement means; user transition prediction means; target period estimation means; and output means. The use amount measurement means is to measure an actual use amount which is a power amount actually used. The use transition prediction means is to predict an assumed use transition which is the transition of the power amount to be used later. The target transition estimation means is to obtain a period in which the unit cost of the electricity charge is changed based on the actual use amount and the assumed use transition. The output means is to output a unit cost change period as the electricity charge prediction information.SELECTED DRAWING: Figure 2

Description

  The present invention relates to an electricity rate information prediction system that predicts information (hereinafter referred to as “electricity rate information”) related to an electricity rate for a person who uses electricity (hereinafter referred to as “electric power consumer”).

  In order to use electricity, electricity consumers sign a contract with a power company in the desired electricity rate plan, and according to the contract, a fixed period unit (usually in units of months, hereinafter referred to as the “payment period”) depends on the use. Pay the fee. Therefore, when it is desired to save the electricity bill during the settlement period (for example, the current month), the use of electricity during the settlement period is suppressed.

  However, until now, electric power consumers have been aware of the amount of power used for the first time by a bill from an electric power company, without knowing the means of grasping the amount of power used in the middle of the settlement period. Therefore, even if the target electricity bill for the settlement period is set, it is not possible to determine whether the amount of power used in the middle of the settlement period is appropriate for the target or excessively used. As a result, it was not possible to take measures such as suppressing subsequent use.

  The most common pay-as-you-go plan for an individual electric power consumer is a fee system in which the unit price increases stepwise (usually in multiple steps) according to the amount of power used during the checkout period. That is, by suppressing the amount of power used, it is possible to avoid an increase in unit price during the settlement period (for example, an increase in unit price in the second stage). However, in this case as well, since the validity of the amount of power used in the middle of the settlement period cannot be determined, subsequent use cannot be adjusted.

  On the other hand, in recent years, smart meters are spreading, and it is expected that the number of people who introduce HEMS (Home Energy Management System) will increase. Therefore, in the future, it is considered that many power consumers grasp their own power consumption and adjust the power consumption during the settlement period. Here, in order for the electric power consumer to adjust the amount of electric power used according to the target electric charge or the unit price increase, it is necessary to grasp the electric charge plan that the electric power company has contracted with the electric power company.

  In addition to pay-per-use plans, electric power companies also offer various electricity rate plans that change unit prices according to changing conditions such as time of day, day of the week, and season. Furthermore, in 2016, entry into the electric retail business will be fully liberalized. Therefore, it is assumed that a wider variety of electricity rate plans will be provided. In other words, while electricity consumers have more choices, it will be difficult to select the best electricity rate plan, and it will be difficult to accurately grasp their contract details. Patent Document 1 proposes a technique for selecting an optimal electricity rate plan from a variety of electricity rate plans in consideration of consumption characteristics of power consumers.

Japanese Patent No. 5717113

  By the way, there is a technology that predicts and provides information such as whether or not the target electricity rate is likely to be exceeded during the settlement period, when the electricity rate used as an indicator will be reached, and when the unit price will rise. If so, it is convenient to take measures such as adjusting the amount of power used, but such a technology has not yet been provided even in the present situation where smart meters and HEMS are becoming widespread.

  An object of the present invention is to solve the problems of the prior art, that is, to provide an electricity bill information prediction system that can adjust the amount of power used according to the target electricity bill during the settlement period and the unit price increase during the settlement period It is to be.

  This invention pays attention to the point of estimating various electric bill information based on the estimated transition while estimating the subsequent usage transition based on the electric power consumption in the middle of the settlement period. It is an invention made based on an idea that has not existed before.

  The electricity rate information prediction system of the present invention comprises a usage amount measuring means, a usage transition predicting means, a target time estimating means, and an output means. The usage measurement means measures the “actual usage” that is the amount of power actually used, and the usage transition prediction means predicts the “expected usage transition” that is the transition of the amount of power used in the future. Is. The target time estimation means obtains the time when the unit price of the electricity price changes based on the actual usage amount and the assumed usage transition, and the output means uses the time obtained by the target time estimation means as the electricity charge prediction information. Output. Note that the electricity rate plan in this case is a rate system in which the unit price of the electricity rate changes according to the accumulated power consumption.

  The electricity rate information prediction system according to the present invention may include a usage amount measuring unit, a usage transition predicting unit, a target rate setting unit, a rate transition calculating unit, a target time estimating unit, and an output unit. The target charge setting means sets a “target electricity charge” that is a target electricity charge, and the charge transition calculation means is a transition of the electricity charge that will be applied in the future based on the electricity charge plan and the assumed usage change. This is to calculate the “expected price transition”. The target time estimation means obtains the time when the target electricity rate is reached based on the target electricity rate and the assumed rate transition. In this case, the output means calculates the time obtained by the target time estimation unit as the electricity rate prediction information. Is output as

  The electricity rate information prediction system according to the present invention may include a usage amount measuring unit, a usage transition prediction unit, a rate transition calculation unit, a payment amount estimation unit, and an output unit. The payment amount estimation means obtains the electricity bill at the time of settlement of the electricity bill plan based on the assumed charge transition, and the output means in this case outputs the electricity fee obtained by the payment amount estimation means as the electricity bill prediction information. To do.

  The electricity rate information prediction system of the present invention may further include actual usage amount storage means and weather data storage means. The actual usage storage means stores the actual usage measured by the usage measurement means, and the meteorological data storage means records the past records of weather data including daily maximum and minimum temperatures and weather. It is stored as “actual weather data”. The use transition prediction means in this case obtains predicted future weather data as “forecast weather data” and extracts actual weather data corresponding to the forecast weather data from the weather data storage means. Further, the usage transition prediction means sets the day corresponding to the extracted actual weather data as a candidate date, reads the actual usage amount of the candidate date from the actual usage storage means, and based on the read actual usage amount Predict expected usage trends.

  The electricity rate information prediction system according to the present invention may further include a consumption characteristic setting unit and an attribute coefficient storage unit. The consumption characteristic setting means is for setting consumption characteristic information (information including the family structure of the electric power consumer or the housing type), and the attribute coefficient storage means is an attribute coefficient (consumption characteristic coefficient set for each consumption characteristic information). And a coefficient including a time characteristic coefficient set for each time). In this case, the usage transition predicting means acquires the consumption characteristic information, extracts the attribute characteristic according to the consumption characteristic information and the consumption time, and also uses the attribute coefficient and the actual usage amount (of the electric rate plan contracted by the power consumer). Estimate usage transition based on the amount used during the current settlement period).

  The electricity rate information prediction system of the present invention may further include power usage model storage means. The power usage model storage means stores at least two types of power usage models, which are preset transition models of power usage. The usage transition predicting means in this case extracts the corresponding power usage model from the power usage model storage means based on the actual usage amount in the current settlement period of the electricity rate plan contracted by the power consumer, and the extracted Predict expected usage based on the power usage model.

  The electricity bill information prediction system of the present invention may further include warning means for outputting warning information when the electricity bill prediction information exceeds a preset threshold value.

  The electricity rate information prediction system of the present invention may further comprise a transmission means, a terminal side reception means, a terminal side output means, and a terminal. The transmission means is for transmitting electricity rate prediction information, the terminal side receiving means is for receiving the electricity rate prediction information transmitted by the transmission means, and the terminal is composed of a terminal side receiving means and a terminal side output means. It is what has.

The electricity rate information prediction system of the present invention has the following effects.
(1) Since it is possible to predict the unit price increase period based on the current power usage status, it is possible to adjust the subsequent power usage, and as a result, it is possible to suppress the payment amount during the settlement period.
(2) Since it is possible to predict when the target payment amount for the settlement period will be reached based on the power usage status up to that time, it is possible to adjust the subsequent power usage, and as a result, the payment amount during the settlement period is suppressed. be able to.
(3) Since the payment amount for the settlement period based on the power usage status up to that time can be predicted, the subsequent power usage can be adjusted, and as a result, the payment amount for the settlement period can be suppressed.

The flowchart which shows the flow of the main processes of the electricity bill information prediction system of this invention. The block diagram explaining the electricity bill information prediction system of this invention. Model diagram explaining assumed usage transition and actual usage transition. The flowchart which shows the flow of the process which estimates an assumed usage transition using a meteorological data. The model figure which shows the performance weather data equivalent to the daily forecast weather data. The model figure which shows the assumed use transition which can be predicted using meteorological data, and the assumed use transition of the period exceeding the number of forecast weather data. The flowchart which shows the flow of the process which estimates assumption use transition by an attribute coefficient. The model figure which shows the assumed use transition using an attribute coefficient. The flowchart which shows the flow of the process which estimates assumption usage transition by an electric power usage model. The model figure explaining the time when a unit price changes. The model figure explaining the time of reaching a target electricity bill. The model figure explaining "payment amount" which is the final settlement amount in the said settlement period.

  An example of an embodiment of the electricity rate information prediction system of the present invention will be described with reference to the drawings.

1. Overall Overview FIG. 1 is a flowchart showing the main processing flow of the electricity rate information prediction system of the present invention. First, an overall outline of the present invention will be described with reference to this flowchart. As shown in this figure, the amount of power actually used by the power consumer (hereinafter referred to as “actual usage”) is measured (Step 10), and the measurement result is stored as needed (Step 20). Then, based on the actual usage up to that time, the transition of the amount of power to be used in the future (hereinafter referred to as “assumed usage transition”) is predicted (Step 30), and the electricity rate information in the settlement period is estimated. The electricity rate information prediction system of the present invention is a method for estimating the unit price change time (Step 40), and a method for estimating the arrival time of the target electricity rate (hereinafter referred to as “target electricity rate”) (Steps 50 to 70). A method (Step 80 to 90) for estimating an electricity bill at the time of settlement (hereinafter simply referred to as “payment amount”), and electricity bill information can be estimated by these three methods (case-1 to case-3). it can. The power consumer can output the estimated electricity bill information (hereinafter referred to as “electric bill forecast information”) (Step 100) and confirm it, and adjust the subsequent power use. Further, the threshold value set in advance and the electricity rate prediction information are compared (Step 110), and if the result is different from the initially assumed result, warning information can be output (Step 120).

  Next, the main configuration of the electricity rate information prediction system of the present invention will be described. FIG. 2 is a block diagram illustrating the electricity rate information prediction system 100 of the present invention. The electricity rate information prediction system 100 can be configured by a terminal 110, a central device 120, and a communication unit that connects the terminal 110 and the central device 120 wirelessly or by wire. In FIG. 2, each means surrounded by the frame of the terminal 110 is a means included in the terminal 110. Similarly, each means surrounded by the frame of the central device 120 is connected to the central device 120. Means included. The central device 120 may be configured using a computer that executes a program.

  The usage amount measuring means 111 measures the actual usage amount, and the measurement result is sent to the central device 120 through the transmission means and stored in the actual usage amount storage means 121. The usage transition prediction unit 122 reads the measurement result (that is, the actual usage amount) from the actual usage amount storage unit 121 and predicts the assumed usage transition based on the actual usage amount. As described above, there are three methods, ie, case-1 to case-3, for obtaining the electricity rate prediction information, and each method refers to the electricity rate plan stored in the rate plan storage unit 123. In case-1, the time when the unit price of the electricity bill changes is estimated by the target time estimating means 124. In case-2, the user (usually a power consumer) sets the target electricity rate using the target rate setting means 125, and the rate transition calculating unit 126 will change the electricity rate that will be applied in the future (hereinafter referred to as "estimated rate transition"). The target time estimation means 124 estimates the time when the target electricity rate is reached. In case-3, the charge transition calculating unit 126 obtains the assumed charge transition, and the payment amount estimating unit 127 estimates the payment amount.

  The electricity rate prediction information is output by the output unit 128 such as a display, and further sent by the transmission unit 129 to the terminal 110 through the communication unit. When the terminal side receiving means 112 of the terminal 110 receives the electricity rate prediction information, the terminal side output means 113 such as a display outputs it. It is also possible to provide warning means 114 for comparing the threshold value set in advance with the electricity rate prediction information and outputting warning information when the result is different from the initially assumed result.

  Hereinafter, it explains in full detail for every main element which comprises the electricity bill information prediction system of this invention.

2. Actual usage amount The actual usage amount is periodically measured by the usage amount measuring means 111 at predetermined time intervals. The measurement time interval can be selected as appropriate. For example, it can be measured every 30 minutes, or can be measured every hour such as every hour. As the usage measuring means 111, various devices can be used as long as they can be measured at a predetermined time interval. However, it is preferable to use smart meters that are being introduced recently. This is because, for example, the smart meter can measure the actual usage at an interval of 30 minutes every day, and can further transmit the measurement data to a predetermined storage means.

  The actual usage amount measured by the usage amount measuring unit 111 is stored in the actual usage amount storage unit 121. At this time, the measured date and time may be stored together with the actual usage value. Specifically, the measured value of actual usage, the measurement date, and time are sequentially accumulated as a set of records (hereinafter referred to as “measurement record”). In addition to the date, the measurement record can include weather information such as temperature, humidity, atmospheric pressure, and climate at the time of measurement. However, when including this weather information, it is desirable to prepare an automatic measuring device, a means for transmitting weather data, and the like separately. Furthermore, the actual usage amount storage unit 121 can also store individual information of the power consumer, that is, user information. Examples of the user information include information such as a family structure, a residential area, and a housing type (detached / collective housing, etc.).

3. Assumed Usage Transition FIG. 3 is a model diagram for explaining an assumed usage transition and an actual usage transition. As shown in this figure, the actual usage transition is a cumulative curve (cumulative transition) of the actual usage amount for each settlement period, and one assumed usage transition is in the current settlement period (hereinafter referred to as “the settlement period”). This is the cumulative transition of the amount of power used in the future. The settlement period is a unit period to be paid for each electricity rate plan as described above, and the settlement period is generally a monthly unit. The usage transition predicting unit 122 automatically predicts the expected usage transition at a predetermined time (for example, the 10th day of the settlement period) or on a regular basis (for example, every hour on the hour). Alternatively, the assumed usage transition can be predicted by the user operating the usage transition prediction means 122.

  The assumed usage transition is predicted based on the actual usage amount (for example, this month) during the settlement period. At this time, the cumulative transition of actual usage can be predicted by statistical processing. For example, a regression curve or regression line is obtained from the cumulative transition slope of actual usage (slope obtained from the bar graph in FIG. 3), and the assumed usage transition is predicted by extending the regression curve or the like. In addition, it is also possible to employ a method of forecasting using weather data, a method of forecasting using a consumption characteristic coefficient or a time characteristic coefficient, and a technique of forecasting using a power usage model. Hereinafter, these methods will be described in order.

(Prediction method based on weather data)
FIG. 4 is a flowchart showing a flow of processing for predicting assumed usage transition using weather data. This will be described in detail below with reference to this figure. First, weather forecast data is acquired (Step 210). Here, “meteorological data” is data for each day including at least one day's maximum and minimum temperatures and weather, and “forecast weather data” is future weather data. The forecast weather data is preferably a plurality of weather data from the next day, and for example, forecast weather data for one week may be acquired from a weekly weather forecast or the like.

  On the other hand, the weather data storage unit 201 stores past weather data as “actual weather data”. It is desirable that the number of actual weather data to be stored is large. For example, actual weather data for the past year may be stored.

  When the forecast weather data is acquired, actual weather data corresponding to the daily forecast weather data is extracted from the weather data storage means 201 (Step 220). That is, past weather data that is the same as the predicted weather is extracted. FIG. 5 is a model diagram showing actual weather data corresponding to daily forecast weather data. As shown in this figure, the actual meteorological data corresponding to one (that is, one day) forecasted meteorological data is not limited to one (ie, one day) actual meteorological data, and a plurality of selected items are selected as candidates. There is also. As described above, the forecast meteorological data includes the daily maximum and minimum temperatures and the weather, and the corresponding actual weather data is selected based on this basic information. At this time, it is conceivable that the actual weather data is not selected in most cases, provided that all the basic information is completely matched. Therefore, the corresponding weather data is selected by relaxing the conditions such as detecting the maximum temperature and the minimum temperature with a margin (buffer) while the conditions are coincidence. As a result, a plurality of actual weather data are selected as candidates for one forecast weather data.

  When the actual weather data is extracted, a day corresponding to the actual weather data is set as a candidate date (Step 230). As shown in FIG. 5, when a plurality of actual weather data are selected as candidates for one forecast weather data, the one closest to the forecast weather data can be selected as a representative candidate, or all of them can be adopted. Can be set as multiple candidate dates. For example, in the case of the third day in FIG. 5, since three actual weather data are selected as candidates, the days corresponding to these are set as candidate days, that is, three candidate days are set.

  When the candidate date is set, the actual usage amount (hereinafter referred to as “daily actual usage amount”) corresponding to the candidate date is read from the actual usage amount storage unit 121 (Step 240). If a plurality of candidate dates are set at this time, a plurality of daily usage amounts are read. Therefore, the actual usage amount storage unit 121 stores the actual daily usage amount for the period (for example, the past one year) stored in the weather data storage unit 201.

  When the daily actual usage amount is read from the actual usage amount storage unit 121, an assumed usage transition is predicted (Step 250). At this time, if the number of candidate dates is one as in the case of the second day in FIG. 5, the actual daily usage read out is for one day, and this actual daily usage is used as it is for the assumed usage transition. be able to. On the other hand, when there are a plurality of candidate days as in the case of the first day and the third day in FIG. 5, since there are a plurality of daily usage amounts to be read out, these are averaged and used. It is used for assumed usage transition after processing. As shown in FIG. 6, the assumed usage transition that can be predicted using the weather data naturally depends on the number of forecasted weather data (for example, for the next one week). Therefore, during a period exceeding the number of forecasted weather data, the expected usage transition may be predicted by other statistical processing such as extending the regression curve.

(Prediction method by attribute coefficient)
FIG. 7 is a flowchart showing the flow of processing for predicting the assumed usage transition based on the attribute coefficient. This will be described in detail below with reference to this figure. In the case of this method, it is necessary to previously store “consumption characteristic information” of the power consumer in the consumption characteristic storage unit 302 by the consumption characteristic setting unit 301. Here, the consumption characteristic information is information including the above-described user information, for example, family structure, residential area, housing type, presence / absence of people staying in the daytime, presence / absence of energy (solar or fuel cell) facilities, This includes information on household appliances (such as air conditioners and kerosene fan heaters) and energy equipment usage characteristics. In addition, since the power usage characteristics vary depending on the power consumer, the consumption characteristic coefficient corresponding to the consumption characteristic information is stored in the attribute coefficient storage unit 303. Furthermore, since the characteristics of power usage change depending on the season or monthly, the time-variable period such as the seasonal characteristic set for each season to reflect this characteristic or the monthly characteristic set for each month. The characteristic coefficient is also stored in the attribute coefficient storage unit 303. Hereinafter, the consumption characteristic coefficient and the time characteristic coefficient are collectively referred to as an “attribute coefficient”.

  As shown in FIG. 7, the consumption characteristic information of the power consumer is first obtained from the consumption characteristic storage means 302 (Step 310). Next, based on the consumption characteristic information and the current time (seasonal or monthly), a corresponding attribute coefficient is extracted from the attribute coefficient storage means 302 (Step 320). Then, the actual usage amount is read from the actual usage amount storage means 121. For example, a regression curve or the like is obtained from the cumulative transition gradient of the actual usage amount, and an attribute coefficient is added to the regression curve or the like as shown in FIG. In consideration of this, a new regression curve or the like is obtained to predict the assumed usage transition (Step 330).

(Prediction method using power usage model)
FIG. 9 is a flowchart showing the flow of processing for predicting the assumed usage transition based on the power usage model. This will be described in detail below with reference to this figure. In the case of this method, it is necessary to store a plurality of types of “power usage models” in the power usage model storage unit 401 in advance. Here, the power usage model is a transition of power consumption during the checkout period, and is composed of, for example, a cumulative value of power usage every hour. The power usage model can represent the power consumption characteristics of a power consumer over a certain period. In this case, it is preferable to prepare a power usage model for each of various power consumption characteristics. Specifically, a large number of user information is collected, systematically organized, and some representative user models are set, and a power usage model is created for each user model. The more a variety of power usage models are prepared, the more appropriately the power usage can be estimated. Alternatively, regardless of the consumption characteristics of the power consumer, all the actual values can be used as the power usage model as they are. The power usage model can be created from past performance values, or can be created by estimating from various conditions.

  As shown in FIG. 9, first, the actual amount used so far in the settlement period is acquired (Step 410). Next, the actual usage transition in the settlement period is obtained based on the actual usage amount, and the power usage model corresponding to the actual usage transition is extracted from the power usage model storage unit 401 (Step 420). Specifically, the actual usage transition is compared with the actual usage transition and the same period of the power usage model, and this is repeated for multiple patterns of power usage models, and the power usage model that is most approximated is selected. To do. For this approximation determination, various methods conventionally used including the least square method can be used. Then, the assumed usage transition is predicted based on the selected power usage model (Step 430).

  The power usage model can be used as the assumed usage transition as it is, but it can also be set as the assumed usage transition after correction to increase or decrease the power usage model in accordance with the actual usage amount of the power consumer. For example, when the actual usage transition and the selected power usage model are compared, the fluctuation (curve) for each time is very close, but it is assumed that there is a difference of about 10% in terms of the usage amount. In this case, it can be easily assumed that the amount of future use will also vary by about 10%. Therefore, the selected power usage model is corrected by 0.9 times (1.1 times), and this increased or decreased model is used. Is assumed usage transition.

4). Electricity rate prediction information As described above, the electricity rate information prediction system of the present invention is a method for estimating unit price change time (case-1) and a method for estimating the arrival time of a target electricity rate as a method for estimating electricity rate information. Three methods of (case-2) and a method of estimating the payment amount (case-3) can be used. Hereinafter, each method will be described in order.

(Estimation of unit price change time)
FIG. 10 is a model diagram for explaining the time when the unit price changes (hereinafter referred to as “unit price change time”). This method is used in the case of a so-called pay-as-you-go system in which the unit price is increased stepwise according to the amount of power used during the settlement period when the electricity rate plan that the power consumer contracts with the power company. As shown in FIG. 10, in this electricity rate plan, the accumulated usage amount in which the unit price changes is determined, and the unit price change time is obtained from the relationship between the accumulated usage amount and the assumed usage transition described so far. In the general pay-as-you-go system, since the unit price changes in a plurality of stages, all unit price change periods may be obtained, or only a specific (for example, second stage) unit price change period may be obtained. The unit price change time obtained here is output as electricity rate prediction information.

(Estimation of target electricity price arrival time)
FIG. 11 is a model diagram for explaining the time to reach the target electricity rate (hereinafter simply referred to as “target time”). In this method, the target fee set by the user (electric power consumer) as a target is set by the target fee setting means 125. The target fee can be set only in one stage, or a plurality of target charges can be set such as two stages as shown in FIG. In addition, using the assumed usage trend described so far and the electricity rate plan that the power consumer contracts, the “estimated rate trend”, which is the future electricity rate change in the settlement period, is obtained. Specifically, the assumed charge transition is calculated by adding the basic charge by multiplying the power consumption of the assumed use transition by the unit price set in the electricity bill plan. Then, the target time is obtained from the relationship between the target charge and the assumed charge transition. The target time obtained here is output as electricity rate prediction information.

(Estimated payment)
FIG. 12 is a model diagram for explaining the “payment amount” that is the final settlement amount in the settlement period. The accumulated usage amount of power until the last day of the settlement period (for example, at the end of the month) is calculated, and an electricity bill corresponding to the usage amount is charged by the power company. In other words, this method estimates the amount billed by the power company. Even in this method, the assumed rate transition is obtained by the same method as “estimating the arrival time of the target electricity rate”. Then, from the fixed rate transition, as shown in FIG. 12, the amount of the last day of the settlement period is obtained as the payment amount. The payment amount obtained here is output as electricity rate prediction information.

5. Warning means The warning means 114 outputs warning information when the electricity rate prediction information exceeds a preset threshold value. When estimating the unit price change time as electricity rate forecast information, an alert (warning information) is output when the unit price change time is estimated earlier than the unit price change time scheduled by the user (electric power consumer) can do. In addition, when estimating the amount of payment as the electricity rate prediction information, an alert may be output when the amount of payment exceeding the amount scheduled by the user (or exceeding a predetermined difference) is estimated. it can. The warning information can be displayed on the display means such as a display together with the warning and the electricity price prediction information. Alternatively, (or in addition), a warning voice or the like is output from the voice output device. You can also.

  The electricity rate plan selection system of the present invention can be used suitably for individual power consumers, and can also be used for business facilities such as factories and offices. The invention of the present application can be said to be an invention that not only can be used industrially but also can make a great social contribution, considering not only the reduction of electricity bills but also the promotion of power consumers' awareness of power saving.

DESCRIPTION OF SYMBOLS 100 Electricity rate information prediction system of this invention 110 Terminal device 111 Usage measuring means 112 Terminal side receiving means 113 Terminal side output means 114 Warning means 120 Central side apparatus 121 Actual usage amount storage means 122 Usage transition prediction means 123 Price plan storage means 124 target time estimation means 125 target charge setting means 126 charge transition calculation means 127 payment amount estimation means 128 output means 129 transmission means 201 weather data storage means 301 consumption characteristic setting means 302 consumption characteristic storage means 303 attribute coefficient storage means 401 power usage model Storage means

  The present invention relates to an electricity rate information prediction system that predicts information (hereinafter referred to as “electricity rate information”) related to an electricity rate for a person who uses electricity (hereinafter referred to as “electric power consumer”).

  In order to use electricity, electricity consumers sign a contract with a power company in the desired electricity rate plan, and according to the contract, a fixed period unit (usually in units of months, hereinafter referred to as the “payment period”) depends on the use. Pay the fee. Therefore, when it is desired to save the electricity bill during the settlement period (for example, the current month), the use of electricity during the settlement period is suppressed.

  However, until now, electric power consumers have been aware of the amount of power used for the first time by a bill from an electric power company, without knowing the means of grasping the amount of power used in the middle of the settlement period. Therefore, even if the target electricity bill for the settlement period is set, it is not possible to determine whether the amount of power used in the middle of the settlement period is appropriate for the target or excessively used. As a result, it was not possible to take measures such as suppressing subsequent use.

  The most common pay-as-you-go plan for an individual electric power consumer is a fee system in which the unit price increases stepwise (usually in multiple steps) according to the amount of power used during the checkout period. That is, by suppressing the amount of power used, it is possible to avoid an increase in unit price during the settlement period (for example, an increase in unit price in the second stage). However, in this case as well, since the validity of the amount of power used in the middle of the settlement period cannot be determined, subsequent use cannot be adjusted.

  On the other hand, in recent years, smart meters are spreading, and it is expected that the number of people who introduce HEMS (Home Energy Management System) will increase. Therefore, in the future, it is considered that many power consumers grasp their own power consumption and adjust the power consumption during the settlement period. Here, in order for the electric power consumer to adjust the amount of electric power used according to the target electric charge or the unit price increase, it is necessary to grasp the electric charge plan that the electric power company has contracted with the electric power company.

In addition to pay-per-use plans, electric power companies also offer various electricity rate plans that change unit prices according to changing conditions such as time of day, day of the week, and season. Furthermore, in 2016, entry into the electric retail business will be fully liberalized. Therefore, it is assumed that a wider variety of electricity rate plans will be provided. In other words, while the choice increases for electricity consumers, will be able to choose the best electricity rate plan becomes difficult, it is considered to be no longer easy to accurately grasp the agreement of their own. Patent Document 1 proposes a technique for selecting an optimal electricity rate plan from a variety of electricity rate plans in consideration of consumption characteristics of power consumers.

Japanese Patent No. 5717113

  By the way, there is a technology that predicts and provides information such as whether or not the target electricity rate is likely to be exceeded during the settlement period, when the electricity rate used as an indicator will be reached, and when the unit price will rise. If so, it is convenient to take measures such as adjusting the amount of power used, but such a technology has not yet been provided even in the present situation where smart meters and HEMS are becoming widespread.

  An object of the present invention is to solve the problems of the prior art, that is, to provide an electricity bill information prediction system that can adjust the amount of power used according to the target electricity bill during the settlement period and the unit price increase during the settlement period It is to be.

  This invention pays attention to the point of estimating various electric bill information based on the estimated transition while estimating the subsequent usage transition based on the electric power consumption in the middle of the settlement period. It is an invention made based on an idea that has not existed before.

  The electricity rate information prediction system of the present invention comprises a usage amount measuring means, a usage transition predicting means, a target time estimating means, and an output means. The usage measurement means measures the “actual usage” that is the amount of power actually used, and the usage transition prediction means predicts the “expected usage transition” that is the transition of the amount of power used in the future. Is. The target time estimation means obtains the time when the unit price of the electricity price changes based on the actual usage amount and the assumed usage transition, and the output means uses the time obtained by the target time estimation means as the electricity charge prediction information. Output. Note that the electricity rate plan in this case is a rate system in which the unit price of the electricity rate changes according to the accumulated power consumption.

  The electricity rate information prediction system according to the present invention may include a usage amount measuring unit, a usage transition predicting unit, a target rate setting unit, a rate transition calculating unit, a target time estimating unit, and an output unit. The target charge setting means sets a “target electricity charge” that is a target electricity charge, and the charge transition calculation means is a transition of the electricity charge that will be applied in the future based on the electricity charge plan and the assumed usage change. This is to calculate the “expected price transition”. The target time estimation means obtains the time when the target electricity rate is reached based on the target electricity rate and the assumed rate transition. In this case, the output means calculates the time obtained by the target time estimation unit as the electricity rate prediction information. Is output as

  The electricity rate information prediction system according to the present invention may include a usage amount measuring unit, a usage transition prediction unit, a rate transition calculation unit, a payment amount estimation unit, and an output unit. The payment amount estimation means obtains the electricity bill at the time of settlement of the electricity bill plan based on the assumed charge transition, and the output means in this case outputs the electricity fee obtained by the payment amount estimation means as the electricity bill prediction information. To do.

  The electricity rate information prediction system of the present invention may further include actual usage amount storage means and weather data storage means. The actual usage storage means stores the actual usage measured by the usage measurement means, and the meteorological data storage means records the past records of weather data including daily maximum and minimum temperatures and weather. It is stored as “actual weather data”. The use transition prediction means in this case obtains predicted future weather data as “forecast weather data” and extracts actual weather data corresponding to the forecast weather data from the weather data storage means. Further, the usage transition prediction means sets the day corresponding to the extracted actual weather data as a candidate date, reads the actual usage amount of the candidate date from the actual usage storage means, and based on the read actual usage amount Predict expected usage trends.

  The electricity rate information prediction system according to the present invention may further include a consumption characteristic setting unit and an attribute coefficient storage unit. The consumption characteristic setting means is for setting consumption characteristic information (information including the family structure of the electric power consumer or the housing type), and the attribute coefficient storage means is an attribute coefficient (consumption characteristic coefficient set for each consumption characteristic information). And a coefficient including a time characteristic coefficient set for each time). In this case, the usage transition predicting means acquires the consumption characteristic information, extracts the attribute characteristic according to the consumption characteristic information and the consumption time, and also uses the attribute coefficient and the actual usage amount (of the electric rate plan contracted by the power consumer). Estimate usage transition based on the amount used during the current settlement period).

  The electricity rate information prediction system of the present invention may further include power usage model storage means. The power usage model storage means stores at least two types of power usage models, which are preset transition models of power usage. The usage transition predicting means in this case extracts the corresponding power usage model from the power usage model storage means based on the actual usage amount in the current settlement period of the electricity rate plan contracted by the power consumer, and the extracted Predict expected usage based on the power usage model.

  The electricity bill information prediction system of the present invention may further include warning means for outputting warning information when the electricity bill prediction information exceeds a preset threshold value.

  The electricity rate information prediction system of the present invention may further comprise a transmission means, a terminal side reception means, a terminal side output means, and a terminal. The transmission means is for transmitting electricity rate prediction information, the terminal side receiving means is for receiving the electricity rate prediction information transmitted by the transmission means, and the terminal is composed of a terminal side receiving means and a terminal side output means. It is what has.

The electricity rate information prediction system of the present invention has the following effects.
(1) Since it is possible to predict the unit price increase period based on the current power usage status, it is possible to adjust the subsequent power usage, and as a result, it is possible to suppress the payment amount during the settlement period.
(2) Since it is possible to predict when the target payment amount for the settlement period will be reached based on the power usage status up to that time, it is possible to adjust the subsequent power usage, and as a result, the payment amount during the settlement period is suppressed. be able to.
(3) Since the payment amount for the settlement period based on the power usage status up to that time can be predicted, the subsequent power usage can be adjusted, and as a result, the payment amount for the settlement period can be suppressed.

The flowchart which shows the flow of the main processes of the electricity bill information prediction system of this invention. The block diagram explaining the electricity bill information prediction system of this invention. Model diagram explaining assumed usage transition and actual usage transition. The flowchart which shows the flow of the process which estimates an assumed usage transition using a meteorological data. The model figure which shows the performance weather data equivalent to the daily forecast weather data. The model figure which shows the assumed use transition which can be predicted using meteorological data, and the assumed use transition of the period exceeding the number of forecast weather data. The flowchart which shows the flow of the process which estimates assumption use transition by an attribute coefficient. The model figure which shows the assumed use transition using an attribute coefficient. The flowchart which shows the flow of the process which estimates assumption usage transition by an electric power usage model. The model figure explaining the time when a unit price changes. The model figure explaining the time of reaching a target electricity bill. The model figure explaining "payment amount" which is the final settlement amount in the said settlement period.

  An example of an embodiment of the electricity rate information prediction system of the present invention will be described with reference to the drawings.

1. Overall Overview FIG. 1 is a flowchart showing the main processing flow of the electricity rate information prediction system of the present invention. First, an overall outline of the present invention will be described with reference to this flowchart. As shown in this figure, the amount of power actually used by the power consumer (hereinafter referred to as “actual usage”) is measured (Step 10), and the measurement result is stored as needed (Step 20). Then, based on the actual usage up to that time, the transition of the amount of power to be used in the future (hereinafter referred to as “assumed usage transition”) is predicted (Step 30), and the electricity rate information in the settlement period is estimated. The electricity rate information prediction system of the present invention is a method for estimating the unit price change time (Step 40), and a method for estimating the arrival time of the target electricity rate (hereinafter referred to as “target electricity rate”) (Steps 50 to 70). A method (Step 80 to 90) for estimating an electricity bill at the time of settlement (hereinafter simply referred to as “payment amount”), and electricity bill information can be estimated by these three methods (case-1 to case-3). it can. The power consumer can output the estimated electricity bill information (hereinafter referred to as “electric bill forecast information”) (Step 100) and confirm it, and adjust the subsequent power use. Further, the threshold value set in advance and the electricity rate prediction information are compared (Step 110), and if the result is different from the initially assumed result, warning information can be output (Step 120).

  Next, the main configuration of the electricity rate information prediction system of the present invention will be described. FIG. 2 is a block diagram illustrating the electricity rate information prediction system 100 of the present invention. The electricity rate information prediction system 100 can be configured by a terminal 110, a central device 120, and a communication unit that connects the terminal 110 and the central device 120 wirelessly or by wire. In FIG. 2, each means surrounded by the frame of the terminal 110 is a means included in the terminal 110. Similarly, each means surrounded by the frame of the central device 120 is connected to the central device 120. Means included. The central device 120 may be configured using a computer that executes a program.

  The usage amount measuring means 111 measures the actual usage amount, and the measurement result is sent to the central device 120 through the transmission means and stored in the actual usage amount storage means 121. The usage transition prediction unit 122 reads the measurement result (that is, the actual usage amount) from the actual usage amount storage unit 121 and predicts the assumed usage transition based on the actual usage amount. As described above, there are three methods, ie, case-1 to case-3, for obtaining the electricity rate prediction information, and each method refers to the electricity rate plan stored in the rate plan storage unit 123. In case-1, the time when the unit price of the electricity bill changes is estimated by the target time estimating means 124. In case-2, the user (usually a power consumer) sets the target electricity rate using the target rate setting means 125, and the rate transition calculating unit 126 will change the electricity rate that will be applied in the future (hereinafter referred to as "estimated rate transition"). The target time estimation means 124 estimates the time when the target electricity rate is reached. In case-3, the charge transition calculating unit 126 obtains the assumed charge transition, and the payment amount estimating unit 127 estimates the payment amount.

  The electricity rate prediction information is output by the output unit 128 such as a display, and further sent by the transmission unit 129 to the terminal 110 through the communication unit. When the terminal side receiving means 112 of the terminal 110 receives the electricity rate prediction information, the terminal side output means 113 such as a display outputs it. It is also possible to provide warning means 114 for comparing the threshold value set in advance with the electricity rate prediction information and outputting warning information when the result is different from the initially assumed result.

  Hereinafter, it explains in full detail for every main element which comprises the electricity bill information prediction system of this invention.

2. Actual usage amount The actual usage amount is periodically measured by the usage amount measuring means 111 at predetermined time intervals. The measurement time interval can be selected as appropriate. For example, it can be measured every 30 minutes, or can be measured every hour such as every hour. As the usage measuring means 111, various devices can be used as long as they can be measured at a predetermined time interval. However, it is preferable to use smart meters that are being introduced recently. This is because, for example, the smart meter can measure the actual usage at an interval of 30 minutes every day, and can further transmit the measurement data to a predetermined storage means.

  The actual usage amount measured by the usage amount measuring unit 111 is stored in the actual usage amount storage unit 121. At this time, the measured date and time may be stored together with the actual usage value. Specifically, the measured value of actual usage, the measurement date, and time are sequentially accumulated as a set of records (hereinafter referred to as “measurement record”). In addition to the date, the measurement record can include weather information such as temperature, humidity, atmospheric pressure, and climate at the time of measurement. However, when including this weather information, it is desirable to prepare an automatic measuring device, a means for transmitting weather data, and the like separately. Furthermore, the actual usage amount storage unit 121 can also store individual information of the power consumer, that is, user information. Examples of the user information include information such as a family structure, a residential area, and a housing type (detached / collective housing, etc.).

3. Assumed Usage Transition FIG. 3 is a model diagram for explaining an assumed usage transition and an actual usage transition. As shown in this figure, the actual usage transition is a cumulative curve (cumulative transition) of the actual usage amount for each settlement period, and one assumed usage transition is in the current settlement period (hereinafter referred to as “the settlement period”). This is the cumulative transition of the amount of power used in the future. The settlement period is a unit period to be paid for each electricity rate plan as described above, and the settlement period is generally a monthly unit. The usage transition predicting unit 122 automatically predicts the expected usage transition at a predetermined time (for example, the 10th day of the settlement period) or on a regular basis (for example, every hour on the hour). Alternatively, the assumed usage transition can be predicted by the user operating the usage transition prediction means 122.

  The assumed usage transition is predicted based on the actual usage amount (for example, this month) during the settlement period. At this time, the cumulative transition of actual usage can be predicted by statistical processing. For example, a regression curve or regression line is obtained from the cumulative transition slope of actual usage (slope obtained from the bar graph in FIG. 3), and the assumed usage transition is predicted by extending the regression curve or the like. In addition, it is also possible to employ a method of forecasting using weather data, a method of forecasting using a consumption characteristic coefficient or a time characteristic coefficient, and a technique of forecasting using a power usage model. Hereinafter, these methods will be described in order.

(Prediction method based on weather data)
FIG. 4 is a flowchart showing a flow of processing for predicting assumed usage transition using weather data. This will be described in detail below with reference to this figure. First, weather forecast data is acquired (Step 210). Here, “meteorological data” is data for each day including at least one day's maximum and minimum temperatures and weather, and “forecast weather data” is future weather data. The forecast weather data is preferably a plurality of weather data from the next day, and for example, forecast weather data for one week may be acquired from a weekly weather forecast or the like.

  On the other hand, the weather data storage unit 201 stores past weather data as “actual weather data”. It is desirable that the number of actual weather data to be stored is large. For example, actual weather data for the past year may be stored.

  When the forecast weather data is acquired, actual weather data corresponding to the daily forecast weather data is extracted from the weather data storage means 201 (Step 220). That is, past weather data that is the same as the predicted weather is extracted. FIG. 5 is a model diagram showing actual weather data corresponding to daily forecast weather data. As shown in this figure, the actual meteorological data corresponding to one (that is, one day) forecasted meteorological data is not limited to one (ie, one day) actual meteorological data, and a plurality of selected items are selected as candidates. There is also. As described above, the forecast meteorological data includes the daily maximum and minimum temperatures and the weather, and the corresponding actual weather data is selected based on this basic information. At this time, it is conceivable that the actual weather data is not selected in most cases, provided that all the basic information is completely matched. Therefore, the corresponding weather data is selected by relaxing the conditions such as detecting the maximum temperature and the minimum temperature with a margin (buffer) while the conditions are coincidence. As a result, a plurality of actual weather data are selected as candidates for one forecast weather data.

  When the actual weather data is extracted, a day corresponding to the actual weather data is set as a candidate date (Step 230). As shown in FIG. 5, when a plurality of actual weather data are selected as candidates for one forecast weather data, the one closest to the forecast weather data can be selected as a representative candidate, or all of them can be adopted. Can be set as multiple candidate dates. For example, in the case of the third day in FIG. 5, since three actual weather data are selected as candidates, the days corresponding to these are set as candidate days, that is, three candidate days are set.

  When the candidate date is set, the actual usage amount (hereinafter referred to as “daily actual usage amount”) corresponding to the candidate date is read from the actual usage amount storage unit 121 (Step 240). If a plurality of candidate dates are set at this time, a plurality of daily usage amounts are read. Therefore, the actual usage amount storage unit 121 stores the actual daily usage amount for the period (for example, the past one year) stored in the weather data storage unit 201.

  When the daily actual usage amount is read from the actual usage amount storage unit 121, an assumed usage transition is predicted (Step 250). At this time, if the number of candidate dates is one as in the case of the second day in FIG. 5, the actual daily usage read out is for one day, and this actual daily usage is used as it is for the assumed usage transition. be able to. On the other hand, when there are a plurality of candidate days as in the case of the first day and the third day in FIG. 5, since there are a plurality of daily usage amounts to be read out, these are averaged and used. It is used for assumed usage transition after processing. As shown in FIG. 6, the assumed usage transition that can be predicted using the weather data naturally depends on the number of forecasted weather data (for example, for the next one week). Therefore, during a period exceeding the number of forecasted weather data, the expected usage transition may be predicted by other statistical processing such as extending the regression curve.

(Prediction method by attribute coefficient)
FIG. 7 is a flowchart showing the flow of processing for predicting the assumed usage transition based on the attribute coefficient. This will be described in detail below with reference to this figure. In the case of this method, it is necessary to previously store “consumption characteristic information” of the power consumer in the consumption characteristic storage unit 302 by the consumption characteristic setting unit 301. Here, the consumption characteristic information is information including the above-described user information, for example, family structure, residential area, housing type, presence / absence of people staying in the daytime, presence / absence of energy (solar or fuel cell) facilities, This includes information on household appliances (such as air conditioners and kerosene fan heaters) and energy equipment usage characteristics. In addition, since the power usage characteristics vary depending on the power consumer, the consumption characteristic coefficient corresponding to the consumption characteristic information is stored in the attribute coefficient storage unit 303. Furthermore, since the characteristics of power usage change depending on the season or monthly, the time-variable period such as the seasonal characteristic set for each season to reflect this characteristic or the monthly characteristic set for each month. The characteristic coefficient is also stored in the attribute coefficient storage unit 303. Hereinafter, the consumption characteristic coefficient and the time characteristic coefficient are collectively referred to as an “attribute coefficient”.

  As shown in FIG. 7, the consumption characteristic information of the power consumer is first obtained from the consumption characteristic storage means 302 (Step 310). Next, based on the consumption characteristic information and the current time (seasonal or monthly), a corresponding attribute coefficient is extracted from the attribute coefficient storage means 302 (Step 320). Then, the actual usage amount is read from the actual usage amount storage means 121. For example, a regression curve or the like is obtained from the cumulative transition gradient of the actual usage amount, and an attribute coefficient is added to the regression curve or the like as shown in FIG. In consideration of this, a new regression curve or the like is obtained to predict the assumed usage transition (Step 330).

(Prediction method using power usage model)
FIG. 9 is a flowchart showing the flow of processing for predicting the assumed usage transition based on the power usage model. This will be described in detail below with reference to this figure. In the case of this method, it is necessary to store a plurality of types of “power usage models” in the power usage model storage unit 401 in advance. Here, the power usage model is a transition of power consumption during the checkout period, and is composed of, for example, a cumulative value of power usage every hour. The power usage model can represent the power consumption characteristics of a power consumer over a certain period. In this case, it is preferable to prepare a power usage model for each of various power consumption characteristics. Specifically, a large number of user information is collected, systematically organized, and some representative user models are set, and a power usage model is created for each user model. The more a variety of power usage models are prepared, the more appropriately the power usage can be estimated. Alternatively, regardless of the consumption characteristics of the power consumer, all the actual values can be used as the power usage model as they are. The power usage model can be created from past performance values, or can be created by estimating from various conditions.

  As shown in FIG. 9, first, the actual amount used so far in the settlement period is acquired (Step 410). Next, the actual usage transition in the settlement period is obtained based on the actual usage amount, and the power usage model corresponding to the actual usage transition is extracted from the power usage model storage unit 401 (Step 420). Specifically, the actual usage transition is compared with the actual usage transition and the same period of the power usage model, and this is repeated for multiple patterns of power usage models, and the power usage model that is most approximated is selected. To do. For this approximation determination, various methods conventionally used including the least square method can be used. Then, the assumed usage transition is predicted based on the selected power usage model (Step 430).

  The power usage model can be used as the assumed usage transition as it is, but it can also be set as the assumed usage transition after correction to increase or decrease the power usage model in accordance with the actual usage amount of the power consumer. For example, when the actual usage transition and the selected power usage model are compared, the fluctuation (curve) for each time is very close, but it is assumed that there is a difference of about 10% in terms of the usage amount. In this case, it can be easily assumed that the amount of future use will also vary by about 10%. Therefore, the selected power usage model is corrected by 0.9 times (1.1 times), and this increased or decreased model is used. Is assumed usage transition.

4). Electricity rate prediction information As described above, the electricity rate information prediction system of the present invention is a method for estimating unit price change time (case-1) and a method for estimating the arrival time of a target electricity rate as a method for estimating electricity rate information. Three methods of (case-2) and a method of estimating the payment amount (case-3) can be used. Hereinafter, each method will be described in order.

(Estimation of unit price change time)
FIG. 10 is a model diagram for explaining the time when the unit price changes (hereinafter referred to as “unit price change time”). This method is used in the case of a so-called pay-as-you-go system in which the unit price is increased stepwise according to the amount of power used during the settlement period when the electricity rate plan that the power consumer contracts with the power company. As shown in FIG. 10, in this electricity rate plan, the accumulated usage amount in which the unit price changes is determined, and the unit price change time is obtained from the relationship between the accumulated usage amount and the assumed usage transition described so far. In the general pay-as-you-go system, since the unit price changes in a plurality of stages, all unit price change periods may be obtained, or only a specific (for example, second stage) unit price change period may be obtained. The unit price change time obtained here is output as electricity rate prediction information.

(Estimation of target electricity price arrival time)
FIG. 11 is a model diagram for explaining the time to reach the target electricity rate (hereinafter simply referred to as “target time”). In this method, the target fee set by the user (electric power consumer) as a target is set by the target fee setting means 125. The target fee can be set only in one stage, or a plurality of target charges can be set such as two stages as shown in FIG. In addition, using the assumed usage trend described so far and the electricity rate plan that the power consumer contracts, the “estimated rate trend”, which is the future electricity rate change in the settlement period, is obtained. Specifically, the assumed charge transition is calculated by adding the basic charge by multiplying the power consumption of the assumed use transition by the unit price set in the electricity bill plan. Then, the target time is obtained from the relationship between the target charge and the assumed charge transition. The target time obtained here is output as electricity rate prediction information.

(Estimated payment)
FIG. 12 is a model diagram for explaining the “payment amount” that is the final settlement amount in the settlement period. The accumulated usage amount of power until the last day of the settlement period (for example, at the end of the month) is calculated, and an electricity bill corresponding to the usage amount is charged by the power company. In other words, this method estimates the amount billed by the power company. Even in this method, the assumed rate transition is obtained by the same method as “estimating the arrival time of the target electricity rate”. Then, from the fixed rate transition, as shown in FIG. 12, the amount of the last day of the settlement period is obtained as the payment amount. The payment amount obtained here is output as electricity rate prediction information.

5. Warning means The warning means 114 outputs warning information when the electricity rate prediction information exceeds a preset threshold value. When estimating the unit price change time as electricity rate forecast information, an alert (warning information) is output when the unit price change time is estimated earlier than the unit price change time scheduled by the user (electric power consumer) can do. In addition, when estimating the amount of payment as the electricity rate prediction information, an alert may be output when the amount of payment exceeding the amount scheduled by the user (or exceeding a predetermined difference) is estimated. it can. The warning information can be displayed on the display means such as a display together with the warning and the electricity price prediction information. Alternatively, (or in addition), a warning voice or the like is output from the voice output device. You can also.

  The electricity rate plan selection system of the present invention can be used suitably for individual power consumers, and can also be used for business facilities such as factories and offices. The invention of the present application can be said to be an invention that not only can be used industrially but also can make a great social contribution, considering not only the reduction of electricity bills but also the promotion of power consumers' awareness of power saving.

DESCRIPTION OF SYMBOLS 100 Electricity rate information prediction system of this invention 110 Terminal device 111 Usage measuring means 112 Terminal side receiving means 113 Terminal side output means 114 Warning means 120 Central side apparatus 121 Actual usage amount storage means 122 Usage transition prediction means 123 Price plan storage means 124 target time estimation means 125 target charge setting means 126 charge transition calculation means 127 payment amount estimation means 128 output means 129 transmission means 201 weather data storage means 301 consumption characteristic setting means 302 consumption characteristic storage means 303 attribute coefficient storage means 401 power usage model Storage means

Claims (8)

In the electricity rate information prediction system that predicts information about electricity rates,
The electricity bill plan contracted by the power consumer is a billing system in which the unit price of the electricity bill changes according to the accumulated power consumption,
Usage measurement means for measuring the actual usage, which is the amount of power actually used;
A usage transition prediction means for predicting an assumed usage transition, which is a transition in the amount of power used in the future,
Based on the actual usage amount and the assumed usage transition, target time estimation means for obtaining a time when the unit price of the electricity bill changes;
An electricity price information prediction system comprising: output means for outputting the time determined by the target time estimation means as electricity price prediction information. In the electricity rate information prediction system that predicts information about electricity rates,
Usage measurement means for measuring the actual usage, which is the amount of power actually used;
A usage transition prediction means for predicting an assumed usage transition, which is a transition in the amount of power used in the future,
Target charge setting means for setting a target electricity charge that is a target electricity charge;
A rate transition calculating means for obtaining a transition of an assumed rate that is a transition of a future rate of electricity based on the rate plan of electricity contracted by a power consumer and the assumed usage transition;
Based on the target electricity charge and the assumed charge transition, target time estimating means for obtaining a time to reach the target electricity charge;
An electricity price information prediction system comprising: output means for outputting the time determined by the target time estimation means as electricity price prediction information. In the electricity rate information prediction system that predicts information about electricity rates,
Usage measurement means for measuring the actual usage, which is the amount of power actually used;
A usage transition prediction means for predicting an assumed usage transition, which is a transition in the amount of power used in the future,
A rate transition calculating means for obtaining a transition of an assumed rate that is a transition of a future rate of electricity based on the rate plan of electricity contracted by a power consumer and the assumed usage transition;
Based on the assumed rate transition, a payment amount estimating means for obtaining an electricity rate at the time of settlement of the electricity rate plan,
An electricity rate information prediction system comprising: output means for outputting the electricity rate obtained by the payment amount estimation unit as electricity rate prediction information. Actual usage storage means for storing the actual usage measured by the usage measurement means;
Weather data storage means for storing past records of weather data including daily maximum and minimum temperatures and weather as actual weather data; and
The usage transition prediction means acquires the predicted future weather data as forecast weather data, extracts the actual weather data corresponding to the forecast weather data from the weather data storage means, and extracts the extracted The date corresponding to the actual weather data is set as a candidate date, the actual usage amount on the candidate date is read from the actual usage amount storage means, and the assumed usage transition is predicted based on the read actual usage amount The electricity rate information prediction system according to any one of claims 1 to 3, wherein Consumption characteristic setting means for setting consumption characteristic information including the family structure of the electric power consumer or the housing type;
An attribute coefficient storage means for storing an attribute coefficient including a consumption characteristic coefficient set for each of the consumption characteristic information and a timing characteristic coefficient set for each period;
The usage transition predicting means acquires the consumption characteristic information, extracts the attribute coefficient according to the consumption characteristic information and the consumption time, and sets the attribute coefficient and the current settlement of the electricity rate plan that the power consumer contracts. The electricity rate information prediction system according to any one of claims 1 to 3, wherein the assumed usage transition is predicted based on the actual usage amount in a period. A power usage model storage means for storing two or more types of power usage models which are preset transition models of power usage;
The usage transition prediction unit extracts the corresponding power usage model from the power usage model storage unit based on the actual usage amount in the current settlement period of the electricity rate plan that the power consumer contracts, and the extracted 4. The electricity rate information prediction system according to claim 1, wherein the assumed usage transition is predicted based on a power usage model.   The electricity bill information prediction according to any one of claims 1 to 6, further comprising warning means for outputting warning information when the electricity bill prediction information exceeds a preset threshold value. system. Transmitting means for transmitting the electricity rate prediction information;
Terminal-side receiving means for receiving the electricity price prediction information transmitted by the transmitting means;
Terminal-side output means for outputting the electricity rate prediction information received by the receiving means;
8. The electricity rate information prediction system according to claim 1, further comprising a terminal having the terminal side receiving means and the terminal side output means.

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