Scheduling system and scheduling program

Abstract

We provide a scheduling system and scheduling program that create an energy usage schedule that can reduce energy costs without changing the lifestyle of consumers. [Solution] The system includes an estimation task 161 that estimates the lifestyle 3 of customer P based on the amount of electricity used measured at predetermined intervals by a smart meter 2 installed at customer P's home, a pricing plan 5 in which the energy usage charges fluctuate according to the time of day in conjunction with the market price in the wholesale electricity market 4, and a scheduling task 162 that creates an electricity usage schedule based on the lifestyle 3, such that at least a portion of the time of day when customer P uses electricity is in line with the lifestyle 3 and is a time when usage charges are low.

Description

Technical Field

[0001] The present invention relates to a scheduling system and a scheduling program for creating an energy usage schedule such as electricity for consumers.

Background Art

[0002] A technique for efficiently deriving consumer's life information based on the usage amounts of gas, electricity, and water of consumers is known (for example, see Patent Document 1). In this technique, usage data in which information on the usage time zone and consumer identification information for identifying the consumer are associated with the electricity usage amount, gas usage amount, and tap water usage amount by the consumer is acquired, and based on the acquired usage data, the consumer's life information is output. Then, the output life information is sent to service providers (insurance companies, distributors, caregivers, security companies, health-related companies, local governments, etc.) that provide various services on the condition of obtaining the consumer's permission to use the usage data. The service provider provides services suitable for the consumer based on the sent life information.

[0003] On the other hand, with the full liberalization of electricity retail, many operators (so-called retail electricity operators) entering the electricity retail business have emerged, and it has become possible to provide consumers with a tariff plan (market-linked tariff plan) linked to the wholesale market (for example, see Patent Document 2). In this tariff plan, the per-unit price (electricity tariff per unit) fluctuates in linkage with the market price in the wholesale market, and the electricity tariff is calculated based on the market price at the time when electricity is used and the electricity usage amount. Therefore, by selecting this tariff plan, consumers can devise ways to increase the electricity usage amount during the time zone when the electricity tariff per unit is low and decrease the electricity usage amount during the time zone when the electricity tariff per unit is high, etc., to reduce the electricity tariff.

Prior Art Documents

Patent Documents

[0004]

Patent Document 1

[0005] Incidentally, each consumer has their own lifestyle, and they use (consume) energy such as electricity, water, and gas according to that lifestyle. Therefore, even if there is a system that allows consumers to expect to reduce energy usage costs by adjusting the time of day they use energy, such as the market-linked pricing plan described in Patent Document 2, not many consumers are willing to change their lifestyle to reduce their usage costs. Furthermore, if a market-linked pricing plan is chosen, consumers will need to continuously collect information on fluctuations in electricity rates, which places a heavy burden on them.

[0006] Furthermore, every consumer hopes for lower energy costs, such as electricity, water, and gas. According to the technology described in Patent Document 1, consumers can receive various services that suit their lifestyle, but they cannot receive advice on the crucial matter of energy usage costs.

[0007] Therefore, the present invention aims to provide a scheduling system and scheduling program that can create an energy usage schedule that can reduce energy usage costs without changing the lifestyle of consumers. [Means for solving the problem]

[0008] To solve the above problems, the invention of claim 1 is a scheduling system characterized by comprising: estimation means for estimating the lifestyle of a consumer based on the amount of energy used measured at predetermined intervals by a meter installed in the consumer's home; a pricing plan in which the energy usage fee fluctuates according to the time of day in conjunction with the market price of the energy in the wholesale market; and scheduling means for creating an energy usage schedule based on the lifestyle such that at least a portion of the time of day the consumer uses the energy is in line with the lifestyle and the usage fee is low.

[0009] The invention of claim 2 is characterized in that, in the scheduling system described in claim 1, the energy is electricity, and the system includes a storage battery installed in the customer's home, and the scheduling means creates the usage schedule such that the usage time for charging the storage battery falls within the period of energy use when the usage charges are low, and the storage battery is discharged during the period of high usage charges.

[0010] The invention of claim 3 is characterized in that, in the scheduling system according to claim 1 or 2, the scheduling means includes a trained model that has been machine-learned based on past performance data, such that when the rate plan and the lifestyle are input, the usage schedule is output.

[0011] The invention of claim 4 is a scheduling program characterized in that a computer functions as an estimation means for estimating a consumer's lifestyle based on the amount of energy used measured at predetermined intervals by a meter installed in the consumer's home; a pricing plan in which the energy usage fee fluctuates according to the time of day in conjunction with the market price of the energy in the wholesale market; and the lifestyle, to create an energy usage schedule such that at least a portion of the time of day when the consumer uses the energy is in line with the lifestyle and when the usage fee is low. [Effects of the Invention]

[0012] According to the inventions described in claims 1 and 4, an energy usage schedule is automatically created based on the consumer's estimated lifestyle based on energy consumption and a pricing plan in which energy usage charges fluctuate according to the time of day in conjunction with the market price in the wholesale energy market. This schedule ensures that at least a portion of the consumer's energy usage time is compatible with the consumer's lifestyle and coincides with a time when energy usage charges are lower. As a result, consumers can reduce their energy usage charges without changing their lifestyle by using energy according to the created schedule. Furthermore, since this schedule is automatically created based on the consumer's lifestyle and pricing plan, it reduces the burden on consumers, such as the need to continuously collect information on pricing plans.

[0013] According to the invention described in claim 2, when the energy source is electricity, by utilizing a storage battery, a usage schedule is created so that the battery is charged during off-peak hours when usage rates are low, and the stored electricity is discharged from the battery during off-peak hours when usage rates are high. This effectively shifts a portion of the electricity usage time from off-peak hours to off-peak hours. As a result, it becomes possible to reduce energy usage costs more efficiently.

[0014] According to the invention of claim 3, since the usage schedule is output using the machine-learned extraction learning model, it is possible to more appropriately extract and estimate the usage schedule.

Brief Description of the Drawings

[0015] [Figure 1] It is a diagram showing an example of a customer using the scheduling system according to Embodiment 1 of this invention. [Figure 2] It is a schematic configuration diagram showing the scheduling system according to Embodiment 1 of this invention. [Figure 3] It is a diagram showing an example of data (electricity rate per unit) stored in the fee plan database of the scheduling system in FIG. 1. [Figure 4] It is a diagram showing an example of the lifestyle stored in the lifestyle database of the scheduling system in FIG. 1. [Figure 5] It is a functional block diagram showing the schematic configuration of the extraction learning model of the scheduling system in FIG. 1. [Figure 6] It is a diagram showing an example of a customer using the scheduling system according to Embodiment 2 of this invention.

Modes for Carrying Out the Invention

[0016] Hereinafter, this invention will be described based on the illustrated embodiments.

[0017] (Embodiment 1) FIG. 1 is a diagram showing an example of a customer P using the scheduling system 1 according to this embodiment. This scheduling system 1 is a system that creates a usage schedule of energy such as electricity for the customer P. In this embodiment, the energy is electricity, and this system 1 is operated and managed by the power company Q, which is the provider of the fee plan 5. The case where the customer P uses this system 1 from the terminal device will be mainly described below.

[0018] The consumer P using this scheduling system 1 has installed a smart meter (meter) 2 for measuring the electricity consumption (usage amount) every predetermined time in the consumer's house, and has made a contract with the power company Q, and pays the electricity fee according to a tariff plan 5 (hereinafter referred to as a market-linked tariff plan) in which the electricity usage fee fluctuates according to the time zone in linkage with the market price in the wholesale electricity market 4.

[0019] The smart meter 2 is an instrument for measuring the electricity usage amount used in the consumer's house every predetermined time, and has the same configuration as an off-the-shelf / existing smart meter. That is, it measures the electricity usage amount every 30 minutes, and transmits the measurement data (30-minute value data) and the meter number to the usage amount database 132 in real time via the entrusted related data providing system of the power wide-area operation promotion organization. Therefore, 48 frames of 30-minute value data are transmitted to the usage amount database 132 in one day. Some of the smart meters 2 have a function for measuring the usage amount of each electrical device, and such a smart meter 2 with such a function may be used.

[0020] The market-linked tariff plan 5 is a tariff plan in which the usage fee fluctuates in linkage with the market price of electricity in the wholesale market (so-called spot market) 4 of JEPX (Japan Electric Power Exchange, Incorporated Association). In JEPX, one day is divided into 48 frames, and electricity is sold every 30 minutes. The spot market refers to a market (one-day-ahead market) where transactions of electricity to be delivered the next day are conducted. Here, the "usage fee" of the market-linked tariff plan 5 is, more specifically, composed of a "basic fee" that occurs at a fixed amount every month, a "quantity-of-electricity fee" calculated by multiplying the "unit price of electricity fee" at the time when electricity is used by the electricity usage amount, and a "renewable energy power generation promotion levy" paid to the country. Among these, the "unit price of electricity fee" corresponds to a fee that fluctuates in linkage with the market price in the wholesale market 4. For example, the "unit price of electricity fee" at time T is determined at time (T + 1) based on the market price at time T.

[0021] Figure 2 is a schematic diagram showing the scheduling system 1 according to this embodiment. The scheduling system 1 mainly comprises an input unit 11, a display unit 12, a storage unit 13, a memory 14, a communication unit 15, an estimation task (estimation means) 161, a scheduling task (scheduling means) 162, a learning task 163, and a central processing unit 17 that controls these.

[0022] The scheduling system 1 is configured by installing, for example, a personal computer, the control program (operation system) for the entire device and applications that perform various processing tasks. In this embodiment, we will describe the case where the scheduling system 1 is configured as a single unit and installed at customer P's house. In contrast, the scheduling system 1 may be composed of multiple computer servers, or part or all of it may be installed at a location other than customer P's house.

[0023] The input unit 11 is an interface that receives commands from customers P and inputs information to the scheduling system 1, and is composed of, for example, a keyboard and a mouse.

[0024] The display unit 12 has functions such as displaying information input via the input unit 11 and displaying the processing results of the scheduling system 1, and is configured, for example, as a liquid crystal display.

[0025] The memory unit 13 is a memory area / storage device equipped with the function of storing various information, programs, and data, and is composed of, for example, a hard disk. The memory unit 13 includes a control program for the entire scheduling system 1, a fee plan database 131, a usage database 132, a lifestyle database 133, an extraction learning model 134, and an extraction performance database 135. Databases 131 to 133 will be described here, and the extraction learning model 134 and the extraction performance database 135 will be described later.

[0026] The rate plan database 131 is a database that stores information related to market-linked rate plan 5. Specifically, it stores information on usage charges (basic charge, energy charge, and renewable energy generation promotion surcharge) for market-linked rate plan 5, as well as information on the market price of electricity used to calculate usage charges (especially the energy charge). The market price information of electricity is periodically acquired and stored from JEPX via the transmission-related data provision system of the Organization for Cross-regional Coordination of Transmission Operators.

[0027] The electricity charges stored in the rate plan database 131 are calculated by multiplying the amount of electricity used by the electricity rate unit price, which is determined at predetermined intervals (for example, every hour) based on the market price of electricity from JEPX. This electricity rate unit price fluctuates in conjunction with the market price, for example, as shown in Figure 3. The graph in Figure 3 shows the trend of the electricity rate unit price over a certain day as a line graph, with the horizontal axis representing time (time) and the vertical axis representing the market price (yen / kWh).

[0028] The usage database 132 is a database that stores information on electricity usage measured by a smart meter 2 installed at customer P's home. Specifically, it stores measurement data (30-minute interval data) from the smart meter 2, obtained through the transmission-related data provision system of the Organization for Cross-regional Coordination of Transmission Operators, in chronological order for each identification number and contract number that identify the contract between the power company Q and customer P.

[0029] If the smart meter 2 has the function to measure the electricity consumption of each electrical appliance, the electricity consumption of each appliance will be stored along with the total electricity consumption. The power consumption pattern 1 (typical weekday power consumption pattern) graph in Figure 4, described later, was created based on electricity consumption data measured by the smart meter 2, which has the function to measure the consumption of each electrical appliance, and the consumption of each electrical appliance is shown as a bar graph. Storing the electricity consumption of each electrical appliance allows for a more accurate estimation of the consumer's lifestyle, and when creating an electricity usage schedule, it is possible to identify electronic devices and adjust the time periods of electricity use, thus enabling the creation of a more detailed usage schedule.

[0030] The lifestyle database 133 is a database that stores information about customer P's lifestyle 3. Specifically, it stores customer P's lifestyle 3, which is estimated by the estimation task described later, for each identification number and contract number that identifies the contract between power company Q and customer P.

[0031] Lifestyle 3 is stored as the lifestyle 3 of the consumer P estimated in estimation task 161, which will be described later. Lifestyle 3 may be defined by classifying it into several categories. Possible categories include, for example, family structure (single or with family), work (day shift or night shift), eating habits (cooks at home or eats out), spending habits (frugal or spendthrift), leisure (goes out or stays at home), and time of day characteristics (night owl or early bird).

[0032] Furthermore, the lifestyle database 133 may also store, as reference data, electricity usage data for a typical day (weekday, holiday (at home), holiday (out), etc.) of customer P, extracted from the usage database 132, along with the electricity usage patterns. This makes it possible to create the electricity usage schedule described later more accurately.

[0033] Figure 4 shows an example of lifestyle 3 stored in the lifestyle database 133. In the example in Figure 4, lifestyle 3 and power consumption patterns as reference data are stored for customer Pa (identification number: XXXX). Lifestyle 3 for customer Pa is stored as follows: family structure is single, work is day shift, eats out frequently, has a wasteful spending habit, spends leisure time going out, and has a nocturnal personality. The power consumption patterns as reference data are set as follows: weekdays are pattern 1, holidays (staying at home) are pattern 2, and holidays (going out) are pattern 3. Typical daily electricity usage data for each pattern is also stored (only pattern 1 is shown; data for patterns 2 and 3 are omitted). The electricity usage data showing this power consumption pattern is shown as a line graph showing the trend of daily electricity usage, and as a bar graph showing the usage of each electrical appliance (refrigerator, air conditioner, mobile phone, induction cooktop, television, lighting), with the horizontal axis representing time (time) and the vertical axis representing electricity usage (Wh).

[0034] Memory 14 is a memory area / device that has the function of being a working area for temporarily storing information and data generated when the central processing unit 17 performs processing related to the creation of electricity usage schedules, and is composed of, for example, RAM (Random Access Memory).

[0035] The communication unit 15 is a communication interface that has the function of transmitting and receiving / inputting signals and information transmitted via various wireless / wired communication network lines, including, for example, LAN (Local Area Network) and WAN (Wide Area Network).

[0036] Estimation Task 161 is a task program that estimates the lifestyle 3 of customer P based on the amount of electricity used measured at predetermined intervals by a smart meter 2 installed at customer P's home. Here, the electricity usage data from the smart meter 2 is the electricity usage data (30-minute data) from the smart meter 2 at customer P's home, which is obtained by the communication unit 15 via the transmission-related data provision system of the Organization for Cross-regional Coordination of Transmission Operators and stored in the usage database 132. As an estimation procedure, for example, the daily electricity usage trends of customer P may be graphed (see the power consumption pattern graph in Figure 4), and the graphs may be classified according to similarities, and the lifestyle 3 may be estimated for each category (e.g., family structure, work, meals, spending habits, leisure, time of day characteristics, etc.).

[0037] The estimation task 161 may, for example, use a trained model that has been machine-learned based on past performance data to estimate lifestyle 3, such that when data on multiple past electricity usages of customer P stored in the usage database 132 is input, customer P's lifestyle 3 is output.

[0038] As described in the explanation of the lifestyle database 133, the lifestyle 3 estimated by estimation task 161 is as shown in Figure 4, and an example of this is shown in Figure 4.

[0039] The scheduling task 162 is a task program that creates an electricity usage schedule for customer P based on the market-linked rate plan 5 to which customer P subscribes, which is stored in the rate plan database 131, and customer P's lifestyle 3, which is estimated by the estimation task 161 and stored in the lifestyle database 133. In this embodiment, when customer P inputs a start command from the input unit 11 of the terminal device, the scheduling task 162 is started, and customer P starts the scheduling task 162 every day to create the electricity usage schedule for that day.

[0040] The scheduling task 162 first searches for and extracts the lifestyle 3 of customer P from the lifestyle database 133. For example, if customer P is customer Pa, the lifestyle 3 shown in Figure 4 is extracted.

[0041] The scheduling task 162 extracts the lifestyle 3 and, at the same time, extracts the market-linked rate plan 5 to which customer P is subscribed from the rate plan database 131. For example, when creating an electricity usage schedule for the day the scheduling task 162 is activated, it extracts information including the electricity rate per unit for that day (see Figure 3), which is determined based on the market price of electricity for that day.

[0042] Next, the scheduling task 162 creates an electricity usage schedule based on the extracted lifestyle 3 information and market-linked pricing plan 5 information, such that at least a portion of customer P's electricity usage time is compatible with customer P's lifestyle 3 and falls within the time period when electricity rates are lower.

[0043] Here, creating a usage schedule that "suits lifestyle 3" means creating a usage schedule on the premise that customer P's lifestyle 3 will not be changed. For example, for customer P, whose lifestyle 3 is working a day shift, a schedule would not be created that causes them to use electricity (at home) during the daytime on weekdays, simply because the electricity rates are cheapest during that time.

[0044] Furthermore, "electricity usage charges" refers to the usage charges under Market Linked Rate Plan 5, and "times when electricity usage charges are low" refers to the time periods under Market Linked Rate Plan 5 when the unit price of electricity, which fluctuates in line with market prices, is lower.

[0045] Furthermore, "at least a portion of the time period during which electricity is used" specifically means at least a portion of the time period during which one or more electronic devices are used among the electrical equipment used at customer P's home.

[0046] The electricity usage schedule created by scheduling task 162 will be explained in detail below, using the example of a customer Pa shown in Figure 4.

[0047] When customer Pa enters a start command into the input unit 11 of the terminal device on a weekday morning (Month △ Day) to create a schedule for the day's electricity usage, and starts the scheduling task 162, the scheduling task 162 first extracts customer Pa's lifestyle 3 from the lifestyle database 133. According to the extracted lifestyle 3 (see Figure 4), customer Pa has a single-person household, works a day shift, eats out frequently, is a spendthrift, enjoys going out in leisure time, and is a night owl. In addition, according to reference data, customer Pa has three types of power consumption patterns 1-3: weekdays, holidays (at home), and holidays (out).

[0048] The scheduling task 162 extracts lifestyle 3 and, simultaneously, extracts information on the market-linked rate plan 5 to which customer Pa subscribes from the rate plan database 131, in particular the electricity rate per unit on Month Day (here, the electricity rate per unit shown in Figure 3 corresponds to this). According to the extracted information on market-linked rate plan 5, the electricity rate per unit on Month Day was lowest around 11:00, then rose, peaked at 16:00, and then declined.

[0049] Next, scheduling task 162 creates an electricity usage schedule for Month △ Day based on the above lifestyle 3 information of customer Pa and the above market-linked rate plan 5 information (electricity rate per day Month △ Day).

[0050] Therefore, scheduling task 162 creates a usage schedule such that, for example, at least part of the time period during which electrical equipment is used falls after 8 PM, when electricity rates are lower, even if it falls during the nighttime. This usage schedule can be said to be suitable for consumer Pa's lifestyle 3, who is single, works a day shift and is at home on weekday evenings, and has a nocturnal lifestyle.

[0051] Furthermore, scheduling task 162 may create a usage schedule by considering not only the customer Pa's lifestyle 3 and the market-linked rate plan 5 they are subscribed to, but also typical power consumption patterns. In this case, scheduling task 162 creates an electricity usage schedule based on electricity usage (electricity usage stored in usage database 132, or power consumption pattern data stored as reference data in lifestyle database), lifestyle 3, and market-linked rate plan 5. In the example above, for example, since customer Pa has high electricity usage in the 7 PM hour on weekdays, scheduling task 162 creates a usage schedule that shifts the use of electrical appliances in the 7 PM hour to after 8 PM when electricity rates are lower. This makes it possible to create a usage schedule that shifts the usage times of electrical appliances used during peak electricity usage hours to peak electricity rates. This usage schedule can be said to be suitable for customer Pa's lifestyle 3, which consists of a single person, a day shift job, being at home on weekday evenings, and a nocturnal lifestyle.

[0052] Furthermore, if the smart meter 2 installed at customer Pa's home has a function to measure the usage of each electrical appliance, the scheduling task 162 may create a usage schedule by also considering the usage of each electrical appliance. In this case, the scheduling task 162 creates an electricity usage schedule based on the electricity usage (for each electrical appliance) (electricity usage stored in the usage database 132, or power consumption pattern data stored as reference data in the lifestyle database 133), lifestyle 3, and rate plan 5. In the example above, for example, customer Pa uses an induction cooktop in the 7 PM hour on weekdays, and the usage is high, so the scheduling task 162 creates a usage schedule that shifts the usage time of this induction cooktop from the 7 PM hour to the 8 PM hour or later, when the electricity rate is lower. This makes it possible to identify electrical appliances and then create a usage schedule that shifts the usage time of those appliances to a time when the electricity rate is lower. This usage schedule is suitable for customer Pa's lifestyle 3, which is characterized by a single-person household, a daytime job, being at home on weekday evenings, and a nocturnal lifestyle.

[0053] The electricity usage schedule created in this way is displayed on the display unit 12.

[0054] Such a scheduling task 162 may use an extraction learning model 134 that has been machine-learned based on past performance data, so that when a lifestyle 3 stored in the lifestyle database 133 and a market-linked rate plan 5 (especially the electricity rate per unit) stored in the rate plan database 131 are input, an electricity usage schedule is output. In other words, the extraction learning model 134 may be used when extracting the lifestyle 3 of customer P from the lifestyle database 133, extracting the market-linked rate plan 5 (especially the electricity rate per unit) that customer P is subscribed to from the rate plan database 131, and creating an electricity usage schedule for customer P based on these. This extraction learning model 134 is created by the learning task 163.

[0055] In other words, the learning task 163 uses past performance data recorded and stored in the performance database 135 to create a learning model 134 for extraction using a known machine learning algorithm such as a neural network. This performance database 135 for extraction contains customer P(P1, P2, P3...P) as input information. n Information on market-linked pricing plan 5 to which the customer(s) are subscribed, and estimated customer(s) P(P1, P2, P3...P) n Lifestyle 3 of ) and consumer P (P1, P2, P3...P n This is a database in which actual electricity usage schedules created for each customer (P1, P2, P3...P) are recorded and stored. Here, data from the rate plan database 131 and the lifestyle database 133 may be used as past performance data. In addition, past performance data includes customer P(P1, P2, P3...P) n The data may include electricity usage data used to estimate lifestyle 3.

[0056] As shown in Figure 5, this learning task 163 uses machine learning and deep learning with a neural network to create a neural network based on actual data recorded in the extraction performance database 135. For example, the input layer is the market-linked rate plan 5 to which customer P subscribes, stored in the rate plan database 131, and the lifestyle 3 of customer P, stored in the lifestyle database 132. The output layer is the electricity usage schedule created for customer P, and the hidden layer is the analysis process from the input layer to the output layer. Then, the learning task 163 uses the actual data of the extraction learning model 134 as training data to learn various parameters in the hidden layer. In other words, the learning task 163 learns various parameters in the hidden layer so that the electricity usage schedule created for customer P, based on the market-linked rate plan 5 to which customer P subscribes, stored in the rate plan database 131, and the lifestyle 3 of customer P, stored in the lifestyle database 132, is output appropriately. In this case, the learning task 163 may be designed to learn in such a way that the evaluation of the created usage schedule by the consumer P (for example, the evaluation of the effect on reducing usage fees) is high.

[0057] As explained above, according to this embodiment, an electricity usage schedule is automatically created based on the estimated lifestyle 3 of customer P, which is determined based on electricity usage, and the market-linked rate plan 5 (particularly the electricity rate per unit) to which customer P subscribes, so that at least a portion of customer P's electricity usage time is compatible with customer P's lifestyle 3 and falls within the period when electricity usage rates are lower. Therefore, by using energy according to the created usage schedule, customer P can reduce their electricity usage costs without changing their lifestyle 3. Furthermore, since this usage schedule is automatically created based on customer P's lifestyle 3 and the market-linked rate plan 5 to which they subscribe, it is possible to reduce the burden on customer P, such as having to continuously collect information on the market-linked rate plan 5.

[0058] Furthermore, since the usage schedule is output using the machine learning-developed extraction model 134, it becomes possible to extract and estimate the usage schedule more accurately.

[0059] (Embodiment 2) Figure 6 shows this second embodiment, which differs from the first embodiment in that a battery storage system 6 is installed at the customer P1's home. Components similar to those in the first embodiment are denoted by the same reference numerals, and their explanation is omitted.

[0060] In Embodiment 1, depending on the lifestyle 3 of customer P, it may be difficult to shift the time of electricity use to a time when electricity usage rates are lower under the market-linked rate plan 5. For example, in the case of customer Pa mentioned above, the lowest electricity rate per unit on Month Day is around 11:00, but Month Day is a weekday and customer Pa is out at work, so they cannot shift the time of use of electrical appliances in their home, such as a washing machine or bath, to around 11:00.

[0061] Therefore, in this embodiment, by utilizing the storage battery 6, a scheduling system 1 is provided that further enhances the effect of reducing electricity costs using the market-linked pricing plan 5.

[0062] Figure 6 shows an example of a customer P1 using the scheduling system 1 according to this embodiment. In addition to electrical appliances, customer P1's house is equipped with a battery 6 capable of storing electricity.

[0063] When creating an electricity usage schedule for such a customer P1, the scheduling task 162 creates a usage schedule such that the time used to charge the battery 6 is during the time when electricity rates are low, and the time used to discharge the battery 6 is during the time when electricity rates are high. Here, it is assumed that the charging or discharging of the battery 6 can be automatically controlled.

[0064] The following explanation will provide a specific example assuming that a battery storage system 6 is installed at the aforementioned customer Pa's home.

[0065] As in the case described above, when customer Pa inputs a start command to the input unit 11 of the terminal device on the morning of a weekday (Month △th) to create a schedule for the day's electricity usage, and starts the scheduling task 162, the scheduling task 162 first extracts customer Pa's lifestyle 3 from the lifestyle database 133. The extracted lifestyle 3 is as shown in Figure 4. At the same time as extracting lifestyle 3, the scheduling task 162 also extracts the market-linked rate plan 5 that customer Pa is subscribed to from the rate plan database 131, in particular the electricity rate per unit for Month △th. The extracted electricity rate per unit for Month △th is as shown in Figure 3.

[0066] Next, scheduling task 162 creates an electricity usage schedule for Month △ Day based on the above lifestyle 3 information of customer Pa and the above market-linked rate plan 5 information (electricity rate unit price information for Month △ Day). Here, since the electricity rate unit price on Month △ Day is lowest around 11:00, scheduling task 162 creates a usage schedule to charge the battery 6 at 11:00. On the other hand, considering that customer Pa uses electricity in the evening after returning home from work (i.e., to match customer Pa's lifestyle 3), scheduling task 162 creates a usage schedule to reduce the amount of electricity used from the commercial power source in the 18:00-19:00 hour, when electricity rates are highest, by discharging from the battery 6 during that time. By following this usage schedule, customer Pa can reduce their electricity usage costs on Month △ Day.

[0067] As explained above, according to this embodiment, by utilizing the storage battery 6, a usage schedule is created so that the storage battery 6 is charged during off-peak hours when electricity rates are low, and the stored electricity is discharged from the storage battery 6 during off-peak hours when electricity rates are high. This effectively allows for a shift in electricity usage time from off-peak hours to off-peak hours. As a result, electricity usage costs can be reduced more efficiently.

[0068] Although embodiments of this invention have been described in detail above, the specific configuration is not limited to these embodiments, and any design changes, etc., that do not depart from the gist of this invention are also included. For example, in the above embodiment, we described a case in which customer P activates the scheduling task 162 every day to create a schedule for electricity usage for the day, but customer P may also have the task create a schedule for electricity usage for a future day. In that case, the electricity rate used as a reference for creating the usage schedule may be estimated by machine learning based on past performance data of electricity rates, such as temperature, rainfall, wind speed, etc. Also, in the above embodiment, the scheduling system 1 does not incorporate a device to control electrical equipment, etc., at customer P's home, but such a control device may be incorporated into the scheduling system 1 so that electrical equipment, etc., at customer P's home are automatically controlled or remotely operated according to the usage schedule.

[0069] On the other hand, the scheduling system 1 described above may be configured by installing the following scheduling program on a general-purpose computer. That is, the program is characterized in that the computer functions as an estimation means (estimation task 161) that estimates the lifestyle of a consumer based on the amount of energy used measured at predetermined intervals by a meter installed in the consumer's home, a rate plan in which the energy usage fee fluctuates according to the time of day in conjunction with the market price of the energy in the wholesale market, and the lifestyle, and creates an energy usage schedule such that at least a portion of the time of day when the consumer uses the energy is in line with the lifestyle and the usage fee is low. [Explanation of symbols]

[0070] 1. Scheduling System 131 Pricing Plan Database 132 Usage Database 133 Lifestyle Database 134 Extraction training model (pre-trained model) 135 Extraction Data Database 161 Estimation Task (Estimation Method) 162 Scheduling Task (Scheduling Means) 163 Learning Tasks 2. Smart Meters (Measuring Instruments) 3. Lifestyle 4. Wholesale Market 5 Pricing Plans 6. Storage Battery P, Pa, P1 Consumer Q: Power company

Claims

1. An estimation means for estimating the lifestyle of a consumer based on the amount of energy used measured at predetermined intervals by a meter installed in the consumer's home, The system includes a pricing plan in which the energy usage fee fluctuates according to the time of day in conjunction with the market price of the energy in the wholesale energy market, and a scheduling means for creating an energy usage schedule based on the customer's lifestyle, such that at least a portion of the time of day the customer uses energy is in line with their lifestyle and during times when the usage fee is lower. A scheduling system characterized by the following features.

2. The aforementioned energy is electricity, Equipped with a battery storage system installed at the aforementioned customer's home, The scheduling means creates a usage schedule such that the time period for charging the battery falls within the energy usage period when the usage charges are low, and the battery is discharged during the time period when the usage charges are high. The scheduling system according to feature 1.

3. The scheduling means includes a trained model that has been machine-learned based on past performance data, such that when the price plan and the lifestyle are input, the usage schedule is output. The scheduling system according to claim 1 or 2, characterized in that it is the same as described in claim 1 or 2.

4. Computers, An estimation means for estimating the lifestyle of a consumer based on the amount of energy used measured at predetermined intervals by a meter installed in the consumer's home, A pricing plan in which the energy usage fee fluctuates according to the time of day in conjunction with the market price in the wholesale energy market, and a scheduling means that creates an energy usage schedule based on the consumer's lifestyle, such that at least a portion of the time of day the consumer uses energy is in line with their lifestyle and during times when the usage fee is lower. A scheduling program characterized by the following features.

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