Presentation system, presentation method, and program

Abstract

To provide a presentation system which can present information for determining whether to install a solar energy facility.SOLUTION: A presentation system 10 includes: a machine learning model configured to use first electricity rate data, as learning data, related to past actual electricity rate of a first user who uses a first facility with a solar energy facility installed therein; a prediction unit 32e which predicts reductions of electricity rate on the assumption that a solar energy facility is installed in a second facility on the basis of second electricity rate data related to past actual electricity rate of a second user who uses the second facility in which no solar energy facility has been installed; and a presentation unit 32f which presents the predicted reductions of electricity rate.SELECTED DRAWING: Figure 1

Description

[Technical field]

[0001] The present invention relates to a presentation system, a presentation method, and a program. [Background technology]

[0002] Patent Document 1 discloses a technique relating to a method for providing configuration specifications for a hybrid system. [Prior art documents] [Patent documents]

[0003] [Patent Document 1] JP 2003-244841 A Summary of the Invention [Problem to be solved by the invention]

[0004] The present invention provides a presentation system and the like that can present information for deciding whether or not to install a photovoltaic power generation facility. [Means for solving the problem]

[0005] A presentation system according to one embodiment of the present invention includes a memory unit storing a plurality of first electricity rate data relating to actual electricity rates of a first user who uses a first facility in which solar power generation equipment is installed; an acquisition unit that acquires second electricity rate data relating to actual electricity rates of a second user who uses a second facility in which solar power generation equipment is not installed; a prediction unit that predicts an amount of reduction in electricity rates assuming that solar power generation equipment is installed in the second facility based on the acquired second electricity rate data and a machine learning model constructed using the plurality of first electricity rate data as learning data; a similarity calculation unit that calculates a similarity between the acquired second electricity rate data and each of the plurality of first electricity rate data; a grouping processing unit that groups the plurality of first users and the second users based on the calculated similarity; and a presentation unit that presents the predicted amount of reduction in electricity rates and a basis for the prediction that is determined based on the first electricity rate data of the first users who belong to the same group as the second user.

[0006] A presentation method according to one aspect of the present invention is a presentation method executed by a computer, wherein the computer is capable of accessing a memory unit in which a plurality of first electricity rate data relating to actual electricity rates of a first user who uses a first facility in which solar power generation equipment is installed is stored, and the presentation method includes an acquisition step of acquiring second electricity rate data relating to actual electricity rates of a second user who uses a second facility in which solar power generation equipment is not installed, a prediction step of predicting an amount of reduction in electricity rates assuming that solar power generation equipment is installed in the second facility based on the acquired second electricity rate data and a machine learning model constructed using the plurality of first electricity rate data as learning data, a similarity calculation step of calculating a similarity between the acquired second electricity rate data and each of the plurality of first electricity rate data, a grouping step of grouping the plurality of first users and the second users based on the calculated similarity, and a presentation step of presenting the predicted amount of reduction in electricity rates and a basis for the prediction determined based on the first electricity rate data of the first users who belong to the same group as the second user.

[0007] A program according to one aspect of the present invention is a program for causing the computer to execute the presentation method. Effect of the Invention

[0008] The presentation system etc. of the present invention can present information for deciding whether or not to install a photovoltaic power generation facility. [Brief description of the drawings]

[0009] [Figure 1] FIG. 1 is a block diagram showing a functional configuration of a presentation system according to an embodiment. [Diagram 2] FIG. 2 is a diagram showing details of information included in the first electricity rate data. [Diagram 3] FIG. 3 is a flowchart of the operations for constructing a machine learning model. [Figure 4] FIG. 4 is a sequence diagram of an operation for predicting the amount of reduction in electricity charges. [Diagram 5] FIG. 5 is a diagram showing an example of a display screen showing the predicted results of the reduction amount of the electricity charge. [Figure 6] FIG. 6 is a diagram showing an example of a display screen showing the predicted results of the reduction in electricity charges, with the probability displayed. [Figure 7] FIG. 7 is a diagram showing a first modified example of a display screen of the predicted result of the reduction amount of electricity charges. [Figure 8] FIG. 8 is a diagram showing a second modified example of a display screen of the predicted result of the reduction amount of electricity charges. DETAILED DESCRIPTION OF THE PREFERRED EMBODIMENTS

[0010] Hereinafter, the embodiments will be described with reference to the drawings. Note that the embodiments described below are all comprehensive or specific examples. The numerical values, shapes, materials, components, component arrangement and connection forms, steps, and order of steps shown in the following embodiments are merely examples and are not intended to limit the present invention. Furthermore, among the components in the following embodiments, components that are not described in the independent claims will be described as optional components.

[0011] In addition, each drawing is a schematic diagram and is not necessarily a precise illustration. In each drawing, the same reference numerals are used for substantially the same configurations, and duplicated explanations may be omitted or simplified.

[0012] (Embodiment) [composition] First, a configuration of a presentation system according to an embodiment will be described below. Fig. 1 is a block diagram showing a functional configuration of the presentation system according to an embodiment.

[0013] The presentation system 10 is a system that can predict the amount of reduction in electricity charges if a solar power generation system is installed in a house that does not have a solar power generation system, and can present the predicted amount of reduction in electricity charges to the resident of the house. A house is an example of a facility.

[0014] The amount of reduction in electricity charges (reduction effect) when a solar power generation system is installed in a house varies depending on the house and the surrounding environment of the house, so residents often cannot decide to install a solar power generation system. For example, if a solar power generation system is installed in a house on a trial basis and the actual amount of reduction in electricity charges is calculated, the calculated amount of reduction in electricity charges would be useful in deciding whether or not to install a solar power generation system, but this is not realistic.

[0015] In response to this, the presentation system 10 can predict the amount of reduction in electricity charges when a solar power generation system is installed in a house that does not have a solar power generation system, and present the predicted amount of reduction in electricity charges together with the grounds for the prediction. The presentation system 10 includes an information terminal 20 and a reduction amount prediction device 30.

[0016] The information terminal 20 is a user interface device in the presentation system 10, and is used by system users such as, for example, residents of houses that do not have solar power generation equipment installed, or people who recommend the installation of solar power generation equipment to the residents (for example, employees (salesmen) of businesses that sell solar power generation equipment). The information terminal 20 is specifically a personal computer, a smartphone, a tablet terminal, or the like. The information terminal 20 includes an operation receiving unit 21, a display unit 22, a communication unit 23, an information processing unit 24, and a memory unit 25.

[0017] The operation reception unit 21 receives operations from a system user. Specifically, the operation reception unit 21 is realized by a keyboard, a mouse, a touch panel, or the like.

[0018] The display unit 22 displays an input screen for information required for predicting the amount of reduction in electricity charges, a display screen for predicting the amount of reduction in electricity charges, etc. The display unit 22 is realized by a display panel such as a liquid crystal panel or an organic EL panel, for example.

[0019] The communication unit 23 is a communication circuit for the information terminal 20 to communicate with the reduction amount prediction device 30. The communication performed by the communication unit 23 may be wired communication or wireless communication. There is also no particular limitation on the communication standard used for the communication.

[0020] The information processing unit 24 performs information processing such as a process of transmitting information required for predicting the amount of reduction in electricity charges to the reduction amount prediction device 30 and a process of displaying the predicted result of the amount of reduction in electricity charges on the display unit 22. The information processing unit 24 is realized by, for example, a microcomputer, but may also be realized by a processor or a dedicated circuit. The functions of the information processing unit 24 are realized by hardware such as a microcomputer or a processor constituting the information processing unit 24 executing a computer program (software) stored in the storage unit 25.

[0021] The storage unit 25 is a storage device that stores information necessary for information processing related to the initial setting operation of the computer program executed by the information processing unit 24. The storage unit 25 is realized by, for example, a semiconductor memory, but may also be realized by an HDD (Hard Disk Drive).

[0022] The reduction amount prediction device 30 predicts the reduction amount of electricity charges when a solar power generation facility is installed in a house that does not have a solar power generation facility. Specifically, the reduction amount prediction device 30 is a server device (cloud computer) or the like. The reduction amount prediction device 30 includes a communication unit 31, an information processing unit 32, and a storage unit 33.

[0023] The communication unit 31 is a communication module (communication circuit) for the reduction amount prediction device 30 to communicate with the information terminal 20. The communication performed by the communication unit 31 may be wired communication or wireless communication. There is no particular limitation on the communication standard used for the communication.

[0024] The information processing unit 32 performs information processing for predicting the amount of reduction in electricity charges. The information processing unit 32 is specifically realized by a processor or a microcomputer. The information processing unit 32 specifically includes an acquisition unit 32a, a feature amount calculation unit 32b, a similarity calculation unit 32c, a grouping processing unit 32d, a prediction unit 32e, and a presentation unit 32f. The functions of the acquisition unit 32a, the feature amount calculation unit 32b, the similarity calculation unit 32c, the grouping processing unit 32d, the prediction unit 32e, and the presentation unit 32f are realized by a processor or a microcomputer constituting the information processing unit 32 executing a computer program stored in the storage unit 33. The functions of the acquisition unit 32a, the feature amount calculation unit 32b, the similarity calculation unit 32c, the grouping processing unit 32d, the prediction unit 32e, and the presentation unit 32f will be described in detail later.

[0025] The storage unit 33 is a storage device that stores various information for predicting the amount of reduction in electricity charges, such as the computer program executed by the information processing unit 32. Specifically, the storage unit 33 is realized by a HDD, a semiconductor memory, or the like.

[0026] [Electricity bill data] The storage unit 33 of the reduction amount prediction device 30 stores a large number (for the number of people) of first electricity rate data relating to the actual electricity rates of residents (hereinafter also referred to as first residents or first users) of a house where a photovoltaic power generation facility has already been installed, and the data is organized into a database accessible by a computer. The first electricity rate data includes identification information of the first resident and information indicating the monthly electricity rate (for example, information that allows distinction by month, such as an electricity rate of 9,000 yen for October 2022).

[0027] In addition to the above information, the first electricity rate data includes user attribute information, contracted power information, power usage statistics information, equipment specification information, home attribute information, and electricity rate reduction information, etc. Fig. 2 is a diagram showing details of information included in the first electricity rate data.

[0028] The user attribute information is information indicating an address, the number of people in a household, a building type, and energy usage, etc. The energy usage indicates whether only electricity is used or whether electricity and gas are used together.

[0029] The contracted power information is information that indicates an electricity rate plan.

[0030] The power usage statistics information is information that indicates the average power usage during the day, the average power usage per day, the average power usage per month, the annual power usage, the ratio of daytime to nighttime power usage, and the ratio of summer and winter power usage.

[0031] The equipment specification information is information indicating the equipment configuration of the photovoltaic power generation equipment, the panel capacity of the photovoltaic power generation equipment, the rated value of the photovoltaic power generation equipment, etc. The equipment specification information may also include the equipment configuration and rated value of equipment other than the photovoltaic power generation equipment. The equipment other than the photovoltaic power generation equipment is, for example, a heat pump water heater, a battery storage system, an EV (Electric Vehicle) charger, or a fuel cell power generation system.

[0032] The housing attribute information is information indicating the layout, total floor area, exterior surface area, and exterior insulation specifications.

[0033] The electricity bill reduction information is information that indicates how much electricity bills have been reduced by installing a solar power generation system and generating electricity in a house where the solar power generation system is already installed. In the following, the reduction in electricity bills is explained as the reduction in electricity bills per year, but it may be the reduction in electricity bills per unit period, such as per month.

[0034] Of the information described above, the user attribute information, contract power information, power usage statistics information, equipment specification information, and home attribute information are acquired by, for example, conducting a questionnaire survey of the first resident.

[0035] Moreover, the electricity fee reduction amount information is calculated by the information processing unit 32 of the reduction amount prediction device 30. The information processing unit 32 can calculate how much the electricity fee would increase if the solar power generation facility was not installed, based on information such as the amount of power generated by the solar power generation facility, the self-consumption of the amount of power generated, and the ratio of power sold, which are stored in the memory unit 33. The amount of increase in the electricity fee corresponds to the amount of reduction in the electricity fee due to the installation of the solar power generation facility.

[0036] It is not essential that the first electricity rate data includes all of the user attribute information, contract power information, power usage statistics information, equipment specification information, home attribute information, and electricity rate reduction amount information. The first electricity rate data may include at least some of the user attribute information, contract power information, power usage statistics information, equipment specification information, home attribute information, and electricity rate reduction amount information as necessary.

[0037] [Actions for building machine learning models] The information processing unit 32 of the reduction prediction device 30 constructs a machine learning model for predicting the reduction in electricity charges when a solar power generation system is installed in a house that does not have a solar power generation system, and stores the model in the storage unit 33. The operation for constructing the machine learning model will be described below. Fig. 3 is a flowchart of the operation for constructing the machine learning model.

[0038] The acquisition unit 32a of the reduction prediction device 30 acquires the first electricity rate data from the storage unit 33 (S11). The feature amount calculation unit 32b calculates the feature amount by processing information other than the electricity rate reduction amount information included in the first electricity rate data (S12). The information processing unit 32 trains a machine learning model on the first electricity rate data in which the information other than the electricity rate reduction amount information has been processed into the feature amount (S13).

[0039] The above steps S11 to S13 are repeated for the number of pieces of first electricity rate data to build a machine learning model. This machine learning model can output the amount of reduction in the electricity rate using information from the first electricity rate data other than the electricity rate reduction information as input data. In other words, the machine learning model can predict the amount of reduction in the electricity rate when a solar power generation system is installed in a house that does not have a solar power generation system.

[0040] The calculation process of the feature amount in step S12 is not essential and may be performed as necessary. The information processing unit 32 may cause a machine learning model to learn a plurality (a large number) of first electricity price data.

[0041] [Prediction of electricity bill reduction amount] Next, the operation of predicting the amount of reduction in electricity charges will be described with reference to Fig. 4. The operation of predicting the amount of reduction in electricity charges is shown in Fig. 4 as a sequence diagram.

[0042] The operation reception unit 21 of the information terminal 20 receives an input operation of the second electricity rate data from a system user (S21). The second electricity rate data is electricity rate data for a resident of a house where no solar power generation equipment is installed (hereinafter, also referred to as a second resident or a second user), and is the same data as the first electricity rate data, except that electricity rate reduction amount information is not included.

[0043] It should be noted that the equipment specification information in the first electricity rate data is equipment specification information for solar power generation equipment that has already been installed, while the equipment specification information in the second electricity rate data is equipment specification information for solar power generation equipment that is scheduled to be installed in the future.

[0044] The information processing unit 24 of the information terminal 20 transmits the input second electricity rate data to the reduction amount prediction device 30 using the communication unit 23 (S22).

[0045] The communication unit 31 of the reduction prediction device 30 receives the second electricity rate data. The acquisition unit 32a acquires the received second electricity rate data (S23). The feature calculation unit 32b calculates the feature by processing information included in the second electricity rate data (S24). Note that the feature calculation process in step S24 is not essential and may be performed as necessary. The prediction unit 32e predicts the reduction in the electricity rate by inputting the second electricity rate data, the information of which has been processed into the feature, into a machine learning model (S25).

[0046] Next, the feature amount calculation unit 32b calculates feature amounts for each of the plurality of first electricity rate data stored in the storage unit 33 by processing information other than the electricity rate reduction amount information included in the first electricity rate data (S26). Note that the feature amount calculation process of step S26 is not essential and may be performed as necessary. Also, in cases where the first electricity rate data is stored in the storage unit 33 in a format including feature amounts, the feature amount calculation process of step S26 can be omitted.

[0047] The similarity calculation unit 32c calculates the similarity between the second electricity rate data whose information has been processed into feature quantities and each of the plurality of first electricity rate data whose information has been processed into feature quantities (S27). The similarity calculation unit 32c calculates, for example, the cosine similarity between a vector whose elements are the feature quantities included in the second electricity rate data and a vector whose elements are the feature quantities included in the first electricity rate data. Note that any existing method may be used to calculate the similarity.

[0048] The grouping processing unit 32d groups the second resident corresponding to the second electricity rate data and the multiple first resident corresponding to the multiple first electricity rate data based on the calculated similarity (S28). For example, the grouping processing unit 32d determines that the first resident whose similarity is equal to or greater than a predetermined value (i.e., the similarity is relatively high) is in the same group as the second resident. Note that any existing method may be used for grouping (grouping, clustering).

[0049] The presentation unit 32f generates presentation information for presenting (visualizing) the amount of reduction in the electricity charge predicted in step S25, and transmits it to the information terminal 20 using the communication unit 31 (S29). The presentation information includes information necessary for displaying the display screen of Fig. 5 (described later), such as basis information showing the basis of the prediction of the amount of reduction in the predicted electricity charge. The basis information is information determined based on the first electricity charge data (more specifically, the actual amount of reduction in the electricity charge, etc.) of the first user who belongs to the same group as the second user.

[0050] The communication unit 23 of the information terminal 20 receives the presentation information. The information processing unit 24 displays the predicted result of the amount of reduction in the electricity charge on the display unit 22 based on the received presentation information (S30). Fig. 5 is a diagram showing an example of a display screen showing the predicted result of the amount of reduction in the electricity charge.

[0051] 5, the distribution of the actual reduction amount of the electricity charge of the first user who belongs to the same group as the second user is graphed, and the graph (distribution) shows the average value of the reduction amount of the electricity charge. The graph and the average value are an example of the basis of the prediction (visualized basis information).

[0052] The graph also displays the predicted amount of reduction in electricity bills (amount) along with where the second resident's electricity bill reduction (predicted amount of reduction in electricity bills) falls in the distribution (corresponding to "you" in Fig. 5). The graph also displays the actual amount of reduction in electricity bills (amount) along with where the first resident, who has the highest similarity to the second resident, falls in the distribution (corresponding to "Mr. A" in Fig. 5). The actual amount of reduction in electricity bills for the first resident, who has the highest similarity to the second resident, is another example of the basis for the prediction (visualized basis information).

[0053] The graph also displays at least a portion of the first electricity rate data of the first resident who has the highest similarity to the second resident. In the example of Fig. 5, the power generation capacity of the solar power generation facility and the electricity rate plan are displayed. In addition, in the example of Fig. 5, the power generation capacity of the solar power generation facility that the second resident wishes to install and the electricity rate plan are also displayed.

[0054] In this way, the presentation system 10 (reduction amount prediction device 30) can predict the amount of reduction in the electricity fee and present the grounds for the prediction, using a plurality of first electricity rate data. In other words, the presentation system 10 can realize both the prediction of the amount of reduction in the electricity fee using a machine learning model and the presentation of the grounds for the prediction, using the same plurality of first electricity rate data.

[0055] In addition, by presenting the predicted amount of reduction in the second resident's electricity bill in a manner that allows comparison with the actual amount of reduction in the electricity bill of a first resident who is similar to the second resident (the amount of reduction in the electricity bill on which the prediction is based), it is possible to improve the persuasiveness of the predicted amount of reduction in the second resident's electricity bill.

[0056] The system user can specify an interval (upper and lower limits of the amount of reduction in the electricity charge) in the graph of FIG. 5 by operating the operation reception unit 21 of the information terminal 20, and when such a specification is made, the probability of obtaining the amount of reduction in the electricity charge in that interval is displayed on the display unit 22. FIG. 6 is a diagram showing an example of a display screen of the predicted result of the amount of reduction in the electricity charge, with the probability displayed. When the upper limit value (Z1 yen in FIG. 6) and the lower limit value (Y1 yen in FIG. 6) of the interval are changed by operating the operation reception unit 21 of the information terminal 20, the displayed probability is also changed. In this case, the acquisition unit 32a acquires information specifying the interval for the graph from the information terminal 20, and the presentation unit 32f presents the probability of obtaining the amount of reduction in the electricity charge in the specified interval according to the length of the specified interval.

[0057] This allows system users to quantitatively grasp the risk (probability) and return (amount of reduction in electricity bills).

[0058] [Modification 1 of the prediction result display screen] The presentation unit 32f of the reduction prediction device 30 may present, on the presentation screen of the prediction result, a product having a monetary value equivalent to the predicted amount of reduction in the electricity fee, instead of or in addition to the amount of the predicted amount of reduction in the electricity fee. Fig. 7 is a diagram showing a first modified example of such a display screen of the prediction result.

[0059] In this way, if the monetary value that can be obtained by installing solar power generation equipment is converted into a product value and presented, system users can more realistically imagine the benefits they can obtain by installing solar power generation equipment.

[0060] In order to present the products, information associating the amount of reduction in the electricity fee with the product equivalent to the amount of reduction in the electricity fee is stored in advance in the storage unit 33. The presentation unit 32f can present the product having a monetary value equivalent to the predicted amount of reduction in the electricity fee by referring to such information.

[0061] [Modification 2 of the prediction result display screen] The presentation unit 32f of the reduction amount prediction device 30 may present the predicted annual change in the reduction amount of the electricity fee. Fig. 8 is a diagram showing a second modified example of such a display screen of the prediction result.

[0062] 8, the presentation unit 32f presents the predicted annual change in the reduction in the electricity fee, such as the predicted reduction in the electricity fee for the year 2020 if the solar power generation facility is installed in 2020, the predicted reduction in the electricity fee for the year 2021 if the solar power generation facility is installed in 2021, etc. This allows the system user to understand the impact of annual changes in the amount of solar radiation and annual changes in the electricity fee on the reduction in the electricity fee.

[0063] In this case, a conceivable method for predicting the amount of reduction in electricity charges in a specific year is to build a learning model using a plurality of first electricity rate data relating to electricity charges in the specific year as learning data, for example.

[0064] 8 also displays the annual change in the actual amount of reduction in the electricity bill of the first resident who is most similar to the second resident. In this way, the presentation system 10 presents the predicted amount of reduction in the electricity bill of the second resident in a manner that allows comparison with the actual amount of reduction in the electricity bill of the first resident who is similar to the second resident (the amount of reduction in the electricity bill on which the prediction is based), thereby improving the persuasiveness of the predicted amount of reduction in the electricity bill of the second resident.

[0065] 8 or another display screen, the presentation unit 32f may present an annual change in the predicted value of the amount of power generation (total amount of power generation in one year) on the assumption that the solar power generation equipment is installed. In this case, the prediction unit 32e calculates (back-calculates) the predicted value of the amount of power generation using, for example, the predicted value of the amount of reduction in the electricity fee and the second electricity fee data (information indicating the electricity fee plan, etc.).

[0066] The prediction unit 32e may predict the amount of power generation using a machine learning model for predicting the amount of reduction in the electricity fee in the specific year. That is, the machine learning model for predicting the amount of reduction in the electricity fee in the specific year may output a predicted value of the amount of power generation in addition to a predicted value of the amount of reduction in the electricity fee, and in this case, the first electricity fee data for constructing the machine learning model includes information on the amount of power generation. The prediction unit 32e may predict the amount of power generation using another machine learning model for predicting the amount of power generation.

[0067] In this case, the annual change in the actual amount of power generation of the first resident who is most similar to the second resident may also be displayed. In this way, the presentation system 10 presents the predicted value of the amount of power generation when a solar power generation system is installed in the house in which the second resident lives in a manner that allows comparison with the actual amount of power generation (the amount of power generation on which the prediction is based) of the solar power generation system owned by the first resident who is similar to the second resident, thereby improving the persuasiveness of the predicted amount of power generation of the second resident.

[0068] 8 or another display screen, the presentation unit 32f may present the annual changes in the average temperature, the maximum temperature, and the minimum temperature. For example, the presentation unit 32f can present the annual changes in the average temperature, the maximum temperature, and the minimum temperature based on temperature information provided to the reduction amount prediction device 30 from a weather information management server (not shown).

[0069] In this way, by presenting the annual changes in temperature, system users can easily understand the impact of annual changes in temperature on the amount of reduction in electricity charges.

[0070] 8 or another display screen, the presentation unit 32f may present the annual changes in the fuel unit prices of coal, LNG (Liquefied Natural Gas), and crude oil. For example, the presentation unit 32f can present the annual changes in the fuel unit prices of coal, LNG, and crude oil based on fuel unit price information provided to the reduction amount prediction device 30 from a fuel unit price information management server (not shown).

[0071] In this way, by presenting the annual changes in fuel unit prices, system users can easily understand the impact of the annual changes in fuel unit prices on the amount of reduction in electricity charges.

[0072] [Effects, etc.] Inventions derived from the disclosure of this specification are, for example, the following inventions. Hereinafter, the inventions derived from the disclosure of this specification will be described together with the effects and the like obtained by the inventions.

[0073] Invention 1 is a presentation system 10 including a memory unit 33 in which a plurality of first electricity rate data relating to actual electricity rates of a first user who uses a first facility in which solar power generation equipment is installed is stored; an acquisition unit 32a that acquires second electricity rate data relating to actual electricity rates of a second user who uses a second facility in which solar power generation equipment is not installed; a prediction unit 32e that predicts an amount of reduction in electricity rates assuming that solar power generation equipment is installed in the second facility based on the acquired second electricity rate data and a machine learning model constructed using the plurality of first electricity rate data as learning data; a similarity calculation unit 32c that calculates a similarity between the acquired second electricity rate data and each of the plurality of first electricity rate data; a grouping processing unit 32d that groups a plurality of first users and second users based on the calculated similarity; and a presentation unit 32f that presents the predicted amount of reduction in electricity rates and a basis for the prediction that is determined based on the first electricity rate data of a first user who belongs to the same group as the second user.

[0074] Such a presentation system 10 can present a predicted value of the reduction in electricity charges assuming that a solar power generation facility is installed, as a reference for deciding whether or not to install a solar power generation facility. In addition, the presentation system 10 can improve the system user's satisfaction with the predicted value of the reduction in electricity charges by presenting the basis for the prediction.

[0075] Invention 2 is the presentation system 10 of Invention 1, in which the presentation unit 32f presents, as the basis for the prediction, the actual amount of electricity bill reduction due to the installation of solar power generation equipment of a first user belonging to the same group as the second user, and at least a part of the first electricity bill data of the first user.

[0076] Such a presentation system 10 can improve the system user's sense of satisfaction with the predicted electricity bill reduction by presenting the actual amount of electricity bill reduction achieved by a first user similar to a second user through the installation of solar power generation equipment.

[0077] Invention 3 is the presentation system 10 of Invention 1 or 2, in which the presentation unit 32f presents, as a basis for prediction, a graph showing the distribution of actual electricity bill reduction amounts due to the installation of solar power generation equipment for a first user belonging to the same group as a second user.

[0078] Such a presentation system 10 can improve the system user's sense of satisfaction with the predicted electricity bill reduction by graphing and presenting the distribution of the actual electricity bill reduction achieved by a first user similar to a second user through the installation of solar power generation equipment.

[0079] Invention 4 is the presentation system 10 of Invention 3, in which the graph shows the average amount of reduction in electricity charges due to the installation of photovoltaic power generation equipment for first users who belong to the same group as the second users.

[0080] Such a presentation system 10 can improve the system user's satisfaction with the predicted electricity bill reduction by presenting the average value of the actual electricity bill reduction achieved by a first user similar to a second user through the installation of solar power generation equipment.

[0081] Invention 5 is the presentation system 10 according to Invention 3 or 4, in which, when a section for the graph is specified while the graph is being presented, the probability of obtaining a reduction in electricity charges within the specified section is presented according to the length of the specified section.

[0082] Such a presentation system 10 can quantitatively present risk (probability) and return (amount of reduction in electricity charges).

[0083] A sixth aspect of the present invention is the presentation system 10 of any one of the first to fifth aspects of the present invention, wherein the presentation unit 32f presents a product having a monetary value equivalent to the predicted reduction in the electricity fee.

[0084] Such a presentation system 10 can present the monetary value that can be obtained when a photovoltaic power generation facility is installed as a commercial value.

[0085] A seventh aspect of the present invention is the presentation system 10 of any one of the first to sixth aspects, in which the presentation unit 32f presents a yearly change in the predicted amount of reduction in the electricity fee.

[0086] In this way, by presenting the annual changes in the predicted reduction in electricity bills, system users can understand the impact on the reduction in electricity bills of factors such as annual changes in solar radiation and annual changes in electricity bills.

[0087] Invention 8 is a presentation system of Invention 7, in which the prediction unit 32e predicts the amount of power generation assuming that a solar power generation equipment is installed in the second facility based on the acquired second electricity rate data and a machine learning model that is the same as or different from the machine learning model, and the presentation unit 23f presents the annual change in the predicted amount of power generation.

[0088] In this way, by presenting the predicted annual change in power generation amount, system users can easily understand the impact of the annual change in power generation amount on the reduction in electricity charges.

[0089] A ninth aspect of the present invention is the presentation system 10 of the seventh or eighth aspect, in which the presentation unit 32f presents annual changes in the average temperature, the maximum temperature, and the minimum temperature.

[0090] In this way, by presenting the annual changes in temperature, system users can easily understand the impact of annual changes in temperature on the amount of reduction in electricity charges.

[0091] Invention 10 is the presentation system 10 of any one of Inventions 7 to 9, in which the presentation unit 32f presents annual changes in the fuel unit prices of coal, LNG, and crude oil.

[0092] In this way, by presenting the annual changes in fuel unit prices, system users can easily understand the impact of the annual changes in fuel unit prices on the amount of reduction in electricity charges.

[0093] Invention 11 is the presentation system 10 of any of Inventions 1 to 10, further comprising a feature calculation unit 32b that calculates features for each of the multiple first electricity rate data, and a prediction unit 32e that predicts the amount of electricity rate reduction by inputting the calculated features into a machine learning model.

[0094] Such a presentation system 10 can predict the amount of reduction in electricity charges by inputting the calculated feature amount into a machine learning model.

[0095] Invention 12 is a presentation system 10 of any of Inventions 1 to 11, in which the first electricity rate data includes attribute information of the first user, contracted power information, electricity usage statistics information, equipment specification information of equipment installed in the first facility, attribute information of the first facility, and electricity rate reduction information.

[0096] Such a presentation system 10 can construct a machine learning model based on first electricity rate data including attribute information of the first user, contract power information, power usage statistics information, equipment specification information of equipment installed in the first facility, attribute information of the first facility, and electricity rate reduction information.

[0097] Invention 13 is a presentation method executed by a computer such as the presentation system 10 (power reduction prediction device 30), in which the computer can access a storage unit 33 in which a plurality of first electricity rate data related to actual electricity rates of a first user who uses a first facility in which a photovoltaic power generation facility is installed is stored, and the presentation method includes an acquisition step S23 of acquiring second electricity rate data related to actual electricity rates of a second user who uses a second facility in which a photovoltaic power generation facility is not installed, and a machine learning model constructed based on the acquired second electricity rate data and the plurality of first electricity rate data as learning data. The presentation method includes a prediction step S25 of predicting an amount of reduction in electricity bills assuming that solar power generation equipment is installed in the second facility based on the obtained second electricity bill data, a similarity calculation step S27 of calculating similarities between the obtained second electricity bill data and each of the plurality of first electricity bill data, a grouping step S28 of grouping a plurality of first users and second users based on the calculated similarities, and presentation steps S29, S30 of presenting the predicted amount of reduction in electricity bills and the basis for the prediction that is determined based on the first electricity bill data of first users who belong to the same group as the second user.

[0098] This presentation method can present the predicted amount of reduction in electricity charges assuming that the photovoltaic power generation facility is installed, as a criterion for deciding whether or not to install the photovoltaic power generation facility. In addition, the presentation method can improve the system user's satisfaction with the predicted amount of reduction in electricity charges by also presenting the basis for the prediction.

[0099] Invention 14 is a program for causing a computer to execute the presentation method of Invention 13.

[0100] According to such a program, the computer can present a predicted value of the reduction in electricity charges assuming that the photovoltaic power generation equipment is installed, as a basis for deciding whether or not to install the photovoltaic power generation equipment. In addition, the computer can improve the system user's satisfaction with the predicted value of the reduction in electricity charges by presenting the basis for the prediction.

[0101] (Other embodiments) Although the presentation system and the determination method according to the embodiment have been described above, the present invention is not limited to the above embodiment.

[0102] For example, in the above embodiment, the amount of reduction in electricity bills was predicted as a factor in deciding whether or not to install solar power generation equipment in a home, but the amount of reduction in electricity bills may also be predicted as a factor in deciding whether or not to install solar power generation equipment in a facility other than a home.

[0103] Furthermore, in the above embodiment, the presentation system is realized by a plurality of devices, but it may be realized as a single device. For example, the presentation system may be realized as a single device corresponding to the reduction amount prediction device. When the presentation system is realized by a plurality of devices, the components of the presentation system may be distributed in any manner among the plurality of devices. For example, some of the functional components of the reduction amount prediction device may be provided by an information terminal. In other words, some of the processes described as being executed by the reduction amount prediction device may be executed by the information terminal.

[0104] In the above embodiment, the processes executed by a specific processing unit may be executed by another processing unit. The order of multiple processes may be changed, or multiple processes may be executed in parallel.

[0105] In the above embodiment, each component may be realized by executing a software program suitable for each component. Each component may be realized by a program execution unit such as a CPU or a processor reading and executing a software program recorded on a recording medium such as a hard disk or a semiconductor memory.

[0106] Furthermore, each component may be realized by hardware. For example, each component may be a circuit (or an integrated circuit). These circuits may form a single circuit as a whole, or each may be a separate circuit. Furthermore, each of these circuits may be a general-purpose circuit, or a dedicated circuit.

[0107] In addition, the general or specific aspects of the present invention may be realized as a system, an apparatus, a method, an integrated circuit, a computer program, or a recording medium such as a computer-readable CD-ROM. Also, the present invention may be realized as any combination of a system, an apparatus, a method, an integrated circuit, a computer program, and a recording medium. For example, the present invention may be realized as a presentation method executed by a computer such as a presentation system. Also, the present invention may be realized as a program for causing a computer to execute the presentation method, or as a non-transitory computer-readable recording medium on which such a program is stored.

[0108] In addition, the present invention also includes forms obtained by applying various modifications to each embodiment that a person skilled in the art may think of, or forms realized by arbitrarily combining the components and functions of each embodiment within the scope that does not deviate from the spirit of the present invention. [Explanation of symbols]

[0109] 10 Presentation System 20 Information terminal 21 Operation reception section 22 Display section 23, 31 Communications Department 24, 32 Information Processing Section 25, 33 Storage section 30 Reduction amount prediction device 32a Acquisition part 32b Feature Calculation Unit 32c Similarity calculation unit 32d Grouping processing section 32e Prediction Department 32f Presentation section

Claims

1. a storage unit in which a plurality of first electricity rate data relating to actual electricity rates of a first user who uses a first facility in which a solar power generation facility is installed is stored; an acquisition unit that acquires second electricity rate data regarding actual electricity rates of a second user who uses a second facility in which no solar power generation equipment is installed; a prediction unit that predicts an amount of reduction in an electricity bill assuming that a photovoltaic power generation facility is installed in the second facility based on the acquired second electricity bill data and a machine learning model constructed using a plurality of the first electricity bill data as learning data; and a similarity calculation unit that calculates a similarity between the acquired second electricity rate data and each of the plurality of first electricity rate data; a grouping processing unit that groups a plurality of the first users and the second users based on the calculated similarity; a presentation unit that presents the predicted amount of reduction in the electricity charge and a basis for the prediction that is determined based on the first electricity charge data of the first user who belongs to the same group as the second user; Presentation system.

2. The presentation unit presents, as a basis for the prediction, a performance record of an amount of reduction in electricity charges due to the installation of a photovoltaic power generation facility of the first user who belongs to the same group as the second user, and at least a part of the first electricity charge data of the first user. The presentation system of claim 1 .

3. The presentation unit presents, as a basis for the prediction, a graph showing a distribution of actual results of reductions in electricity charges due to the installation of photovoltaic power generation equipment of the first users who belong to the same group as the second users. A presentation system according to claim 1 or 2.

4. The graph shows an average value of the reduction in electricity charges due to the installation of a photovoltaic power generation facility for the first users who belong to the same group as the second users. The presentation system of claim 3 .

5. When a section of the graph is specified while the graph is being presented, the probability of obtaining a reduction in the electricity charge within the specified section is presented according to the length of the specified section. The presentation system of claim 4.

6. The presentation unit presents a product having a monetary value corresponding to the predicted reduction in the electricity fee. A presentation system according to claim 1 or 2.

7. The presentation unit presents the predicted annual change in the amount of reduction in the electricity charge. A presentation system according to claim 1 or 2.

8. The prediction unit predicts an amount of power generation assuming that a photovoltaic power generation facility is installed in the second facility, The presentation unit presents the predicted annual change in the amount of power generation. The presentation system of claim 7.

9. The presentation unit presents annual changes in average temperature, maximum temperature, and minimum temperature. The presentation system of claim 7.

10. The presentation unit presents annual changes in fuel unit prices of coal, LNG (Liquefied Natural Gas), and crude oil. The presentation system of claim 7.

11. Further, a feature amount calculation unit is provided for calculating a feature amount for each of the plurality of first electricity price data, The prediction unit predicts the amount of reduction in the electricity charge by inputting the calculated feature amount into the machine learning model. A presentation system according to claim 1 or 2.

12. The first electricity rate data includes attribute information of the first user, contract power information, power usage statistics information, equipment specification information of equipment installed in the first facility, attribute information of the first facility, and electricity rate reduction amount information. A presentation system according to claim 1 or 2.

13. 1. A computer-implemented presentation method, comprising: the computer is capable of accessing a storage unit in which a plurality of first electricity rate data relating to actual electricity rates of a first user who uses a first facility in which a solar power generation facility is installed is stored; The presentation method includes: An acquisition step of acquiring second electricity rate data regarding actual electricity rates of a second user who uses a second facility in which no solar power generation equipment is installed; a prediction step of predicting an amount of reduction in an electricity bill assuming that a photovoltaic power generation facility is installed in the second facility based on the acquired second electricity bill data and a machine learning model constructed using a plurality of the first electricity bill data as learning data; a similarity calculation step of calculating a similarity between the acquired second electricity rate data and each of the plurality of first electricity rate data; a grouping step of grouping a plurality of the first users and the second users based on the calculated similarity; and a presentation step of presenting the predicted amount of reduction in the electricity charge and a basis for the prediction, the basis being determined based on the first electricity charge data of the first user who belongs to the same group as the second user. Presentation method.

14. A program for causing a computer to execute the presentation method according to claim 13.

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