JP2014124059A - Method of predicting change in electricity storage quantity and predicting device - Google Patents

Abstract

PROBLEM TO BE SOLVED: To provide a predicting method that can predict change in electricity storage quantity of an electricity storage device in a predetermined period in consideration of various conditions according to residential houses in order to optimize panel size of a solar panel and capacity of the electricity storage device or the like.SOLUTION: The predicting method performs: a first step of calculating predicted electricity production Ep(t) of a private electric generator for one prediction period that is one of predetermined periods divided into a plurality of unit periods; a second step of calculating electricity consumption Ec(t) predicted to be consumed by an electricity consuming apparatus; and a third step of calculating value determined by adding a difference after deduction of the electricity consumption Ec(t) from the electricity production Ep(t) to electricity storage quantity BT(t) as electricity storage quantity BT(t+1) at the time of ending the prediction period. All of the unit periods are set as the prediction periods in sequence from the starting time of the predetermined periods through the ending time thereof and the first step, the second step and the third step are repeated for the respective periods so as to predict change in electricity storage quantity in the predetermined periods.

Description

  The present invention relates to a method for predicting the transition of the amount of electricity stored in a power storage device and a prediction device for a house including a private power generation device and the power storage device.

  In recent years, as a part of global warming countermeasures, it is expected to use renewable energy such as solar power generation or wind power generation as a power source for ordinary houses. For example, Patent Literature 1 below describes a house including a solar panel as a private power generation device and a power storage device. In the house, electric power generated by the solar panel is consumed in the daytime, and the electric power stored in the power storage device is consumed at night or when it is cloudy, thereby reducing the amount of power supplied from the system power supply.

  Further, in Patent Document 1 below, the cost of consuming power supplied from the system power supply is offset by selling the power by causing the surplus power in the daytime to flow backwards to the system, and in an economical sense, It also describes the point of self-sufficiency.

  However, when houses as described in Patent Document 1 below become widespread in the future, it is difficult to assume that all such houses will continue to sell electricity. The reason for this is that as reverse power flows from a large number of houses, the voltage and frequency of power supplied from the system power supply cannot be kept constant, and it is physically impossible to reverse power flow. This is because it becomes possible. It is also expected that it will be difficult in the future to maintain policies that require electric power companies to respond to electricity sales.

  Therefore, without consuming the power supplied from the system power supply, all the power consumption can be covered only by the power generated by the private power generation device, that is, for a house that can be self-sufficient in power in a substantial sense, Diligent consideration has been made. For example, if the panel size of the solar panel is increased and the capacity of the power storage device is also increased, a sufficient amount of power can be stored in the power storage device in the daytime. As a result, it is possible to cover the power consumption at night or in cloudy weather only with the power supplied from the power storage device.

  In such a house, the length of the period (for example, the number of days) in which power can be self-sufficient without supplying power from the system power supply is correlated with the panel size of the solar panel and the capacity of the power storage device. Yes. In order to lengthen the period during which power can be self-sufficient, it is desirable to increase the panel size of the solar panel as much as possible. However, in view of the installation cost of the solar panel and the power storage device, the installable area of the solar panel in the house, and the like, it is not desirable to unnecessarily increase the panel size of the solar panel.

  For this reason, when designing a house, the solar panel must be designed to be as long as possible within the range of allowable installation costs. It is necessary to optimize the panel size and the capacity of the power storage device.

  In other words, under the condition that power supply from the system power supply is not performed, the period (number of days) in which the storage amount of the power storage device does not become zero is as long as possible within the range of allowable installation costs. It is necessary to optimize the panel size of the solar panel and the capacity of the power storage device.

JP 2001-81981 A

  In optimizing the panel size of the solar panel and the capacity of the power storage device, it is estimated in advance how the power storage amount will change every day when specific values of the panel size and capacity are adopted. It is desirable to be able to do it. If the transition of the amount of stored electricity can be predicted, optimization of panel size and the like can be easily studied. As an example of an item that becomes a guide when performing the examination, there is a ratio of a day in which the amount of power stored in the power storage device has never become zero in one year.

  However, the amount of power generated by the solar panel varies depending on the panel size, but also varies greatly depending on the location conditions of the house, the weather conditions, the time zone, and the like. In addition, the amount of power used in a house also varies greatly depending on the number of people living in the house, the lifestyle, and the time zone. For this reason, it is not easy to accurately estimate the transition of the amount of power storage and optimize the panel size of the solar panel and the capacity of the power storage device based on this.

  The present invention has been made in view of such problems, and its purpose is to consider various conditions that differ from house to house in order to optimize the panel size of the solar panel, the capacity of the power storage device, and the like. An object of the present invention is to provide a prediction method capable of predicting the transition of the amount of power stored in a power storage device in a predetermined period.

  In order to solve the above problems, a prediction method according to the present invention includes an in-house power generator, a power consuming device that consumes at least part of the power generated by the in-house power generator, and the power generated by the in-house power generator. A power storage device that temporarily stores power that has not been consumed by the power consuming device, and that appropriately supplies power to the power consuming device when the power generated by the private power generation device is insufficient. A method for predicting a change in the amount of power stored in the power storage device in a predetermined period under a condition that power supply from a system power supply is not performed for a house provided, the predetermined period being divided into a plurality of unit periods One of them is set as a prediction target period, and a first step of calculating a power generation amount predicted to be generated by the private power generation apparatus in the prediction target period, and the power in the prediction target period The second step of calculating the amount of power consumption that is expected to be consumed by the expense device, and adding the difference obtained by subtracting the amount of power consumption from the amount of generated power to the amount of electricity stored at the start of the prediction target period A third step of calculating the obtained value as the amount of stored electricity at the end of the prediction target period, and all the unit periods are sequentially calculated from the start to the end of the predetermined period. Going as a period, the transition of the amount of electricity stored in the predetermined period is predicted by repeating the first step, the second step, and the third step for each. In the third step, the difference When the value obtained by adding the value exceeds the maximum power storage amount of the power storage device, the power storage amount is calculated as the same value as the maximum power storage amount, and the value obtained by adding the difference is negative. And the value of When Tsu is characterized in that to calculate the storage amount as 0.

  The prediction method according to the present invention predicts a change in the amount of power stored in a power storage device in a predetermined period under a condition that power supply from a system power supply is not performed for a house including a private power generation device and a power storage device. It is a method. In this prediction method, the predetermined period is divided into a plurality of unit periods, and the transition of the storage amount is predicted by calculating the amount of power that will enter and exit the storage device for each unit period.

  Specifically, all the unit periods included in the predetermined period are sequentially set as the prediction target periods from the start to the end of the predetermined period, and the respective unit periods that are set as the prediction target periods are described below. A process, a second process, and a third process are performed. That is, each of the first step, the second step, and the third step is repeatedly performed by the number of unit periods included in the predetermined period.

  The first step is a step of calculating the amount of generated power that is predicted to be generated by the private power generator during the prediction target period. The second step is a step of calculating a power consumption amount that is predicted to be consumed by the power consuming device in the prediction target period. Since such a 1st process and a 2nd process are performed with respect to all the unit periods, the temporal change of the electric power generation amount and electric power consumption in a predetermined period can be calculated | required.

  Note that “calculate” here refers to an aspect in which the value of the amount of generated power or the amount of power consumed corresponding to the prediction target period is calculated each time the first step, the second step, and the third step are repeated. It is not limited to. For example, the power generation amount or power consumption value is calculated and stored in advance for all unit periods, and in the first step and the second step, the prediction target period is selected from a plurality of stored values. It is also possible to read a value corresponding to.

  The third step adds the difference obtained by subtracting the power consumption amount (calculated in the second step) from the power generation amount (calculated in the first step) to the power storage amount at the start of the forecast period. This is a step of calculating the obtained value as the amount of electricity stored at the end of the prediction target period. Since such a third step is performed for all unit periods, it is possible to calculate the change in the amount of stored electricity over time based on the change over time in the amount of generated power and the amount of consumed power in a predetermined period.

  In the third step, when the value obtained by adding the above differences exceeds the maximum power storage amount of the power storage device, the power storage amount is calculated as the same value as the maximum power storage amount. Further, when the value obtained by adding the difference becomes a negative value, the amount of power storage is calculated as 0. For this reason, the value of the amount of stored electricity is a value from 0 to the maximum amount of stored electricity, and a value of the amount of stored electricity that is impossible is not calculated.

  As an initial condition, for example, if the prediction method of the present invention is performed after setting the panel size of a solar panel as a private power generation device and the capacity of a power storage device, the change in the amount of power stored over time under such conditions is changed. Can be calculated. For this reason, it becomes possible to consider optimization, such as how much the panel size of a solar panel and the capacity | capacitance of an electrical storage apparatus should be enlarged in the range of the installation cost permitted.

  In the prediction method according to the present invention, when the unit period in which the storage amount calculated in the third step is a value other than 0 is defined as an independent unit period, the continuous number of the independent unit period is calculated as follows. It is also preferable to further have a first counting step for measuring.

  In this preferable aspect, it further has the 1st count process which measures the continuous frequency | count of a self-supporting unit period. The “independent unit period” is a unit period in which the amount of power calculated in the third step is a value other than zero. That is, it is a unit period in which the amount of electricity stored at the end point is calculated as zero.

  By having such a first counting step, as one guideline when examining how much self-sufficiency of power can be realized, for example, the length of the period in which power supply from the system power supply is not required is performed. It can be easily calculated.

  In the prediction method according to the present invention, the number of the independent days included in the predetermined period is measured when an entire period in which the plurality of independent unit periods are continuous over 24 hours is defined as an independent day. It is also preferable to further include a second counting step.

  In this preferable aspect, it further has the 2nd count process which measures the number of the independence days included in a predetermined period. An “independence day” is the entire period in which a plurality of independence unit periods are continued for 24 hours. That is, it is one or a plurality of continuous unit periods included in the predetermined period, and is a period in which the storage amount is not calculated as 0 for 24 hours.

  By having such a second counting step, as one guideline when examining how much self-sufficiency of power can be realized, for example, the number of days for which power supply from the system power supply is not performed is a predetermined period ( For example, it is possible to easily calculate the ratio to one year).

  In this preferred embodiment, the start of the independent day is not fixed at a specific time in the day (for example, midnight). That is, for example, the fact that the amount of stored electricity has never been calculated as 0 between midnight and midnight the next day is not a condition for counting the period as an independent day.

  As described above, when the start of the independent day is fixed at midnight, for example, from 1 am to 11 pm on the next day (the length of the period is 46 hours), Even if it is not calculated as 0, the amount of stored power is calculated as 0 between midnight and 1 am on the same day, and between 11 and 12:00 pm (midnight at midnight) on the next day. In such a case, the independence day is not included in the period. However, in view of the fact that the count value for an independent day is one measure when considering how much self-sufficiency of power can be achieved within a predetermined period, such a method for counting an independent day is not appropriate. . In fact, the peak time of power consumption in a house (the time of the day) is not always constant, and it can vary from day to day or every certain period (for example, every season). It is difficult to reflect the actual situation without fixing the time at a specific time in the day.

  In this preferred embodiment, as described above, since the start of the independent day is not fixed at a specific time in the day, the independent day is appropriately counted. As a result, the obtained count value can be used as an appropriate standard when examining how much power self-sufficiency can be realized.

  Further, in the prediction method according to the present invention, the private power generation device has a power generation amount that fluctuates depending on surrounding weather conditions, and a power generation performance setting step in which the power generation performance of the private power generation device is set; and the private power generation A first weather setting step in which a first weather condition is set as a weather condition that affects the power generation amount of the apparatus, and in the first step, the set power generation performance and the first It is also preferable to calculate the amount of generated power based on one weather condition.

  This preferred embodiment is a method for predicting the amount of electricity stored when the private power generation device has a power generation amount that varies depending on the surrounding weather conditions (for example, a solar panel), and includes a power generation performance setting step and a first weather setting step. And further.

  The power generation performance setting step is a step in which the power generation performance of the private power generator is set. In the power generation performance setting step, for example, information (specifications) of the private power generation apparatus regarding the power generation performance such as the panel size of the solar panel is set.

  The first weather setting process is a process in which a weather condition (first weather condition) that affects the power generation amount of the private power generation apparatus is set. In the first weather setting step, for example, the amount of sunlight when the private power generation device is a solar panel, the amount of air when the private power generation device is a wind power generation device, and the like are set.

  In the first step, the amount of generated power is calculated based on the power generation performance set in the power generation performance setting step and the first weather situation set in the first weather setting step. Since the amount of stored electricity is calculated based on changes in the weather information over time at the location of the house and the changes in the amount of power generated over time, it is possible to accurately predict changes in the amount of stored electricity in a form that is closer to the actual situation. It becomes possible.

  In the prediction method according to the present invention, a floor plan setting step for setting the floor plan of the house, a heat insulation performance setting step for setting the heat insulation performance of the house, and a number of person setting step for setting the number of people living in the house, The heating / cooling device setting step for setting the heating / cooling device information including the number of installed heating / cooling devices in the house, the installation location, and the heating / cooling performance, and the weather conditions that affect the heating / cooling load of the house, A second weather setting step in which a second weather condition is set, and in the second step, the set floor plan, the heat insulation performance, the number of people, the heating / cooling equipment information, and the first It is also preferable to calculate the power consumption based on two weather conditions.

  In this preferred embodiment, a floor plan setting process for setting a floor plan for a house, a heat insulation performance setting process for setting the heat insulation performance of the house, a person number setting process for setting the number of people living in the house, and a heating / cooling device in the house The second meteorological condition is set as a heating / cooling apparatus setting process for setting heating / cooling apparatus information including the number of installed units, installation location, and heating / cooling performance, and a weather condition that affects the heating / cooling load of the house. And two weather setting steps. In the second step, the power consumption is calculated based on various information set in these steps.

  For example, in the case where a large number of people live in a large house in a cold region, it is expected that the power consumption in winter will be particularly large because the heating load is large. In this way, taking into account the circumstances specific to each house, the change in power consumption over time is calculated, and the amount of electricity stored is calculated based on this. It becomes possible to predict.

  The prediction method according to the present invention further includes a lifestyle setting step of setting a lifestyle of a person living in the house, and in the second step, the set floor plan, the heat insulating performance, and the number of people are set. It is also preferable to calculate the power consumption based on the heating / cooling device information, the second weather condition, and the lifestyle.

  In this preferable aspect, it has further the lifestyle setting process which sets the lifestyle of the person who lives in a house. In the second step, the amount of power consumption is calculated based on the lifestyle of the person living in the house in addition to various information set in the floor plan setting step and the like. For example, in a house where the whole family is out during the daytime on weekdays, it is expected that the amount of power consumption during the time period will be small. On the other hand, in a house where old couples who rarely go out live, the power consumption is expected to be relatively large even during the daytime on weekdays. In this way, the change in power consumption over time is calculated after taking into account the lifestyles of people living in individual houses, and the amount of electricity stored is calculated based on this. Makes it possible to predict accurately.

  In addition, the prediction device according to the present invention is characterized by predicting the transition of the amount of power stored in the power storage device by the above prediction method and notifying the user of the prediction result.

  ADVANTAGE OF THE INVENTION According to this invention, the prediction apparatus which estimates transition of the electrical storage amount of an electrical storage apparatus and alert | reports the said prediction result to a user is provided. By using such a predicting device, it is possible to accurately indicate the expected transition of the amount of stored electricity when examining the panel size of the solar panel installed in the house and the capacity of the power storage device.

  In the prediction device according to the present invention, it is also preferable to predict a change in the amount of power stored in the power storage device by the prediction method and to notify the user of the number of independent days measured by the second counting step.

  In this preferred embodiment, a prediction device is provided that notifies the user of the number of independent days measured by the second counting step. As a guideline when considering how much power self-sufficiency can be realized, for example, it is easy to calculate the ratio of the number of days that do not require power supply from the grid power supply to a predetermined period (for example, one year) Can be shown.

  According to the present invention, in order to optimize the panel size of the solar panel, the capacity of the power storage device, and the like, the transition of the amount of power stored in the power storage device in a predetermined period is predicted while considering various conditions that differ for each house. It is possible to provide a prediction method capable of

It is a block diagram for demonstrating the prediction method which concerns on one Embodiment of this invention. It is a block diagram for demonstrating the flow of a process at the time of creating a generated electric energy database. It is a block diagram for demonstrating the flow of a process at the time of creating a power consumption database. It is a block diagram for demonstrating the flow of a process at the time of creating a heating / cooling database. It is a block diagram for demonstrating the flow of a process at the time of creating a hot water supply database. It is a block diagram for demonstrating the flow of a process at the time of creating a kitchen database. It is a block diagram for demonstrating the flow of a process at the time of creating a household appliance database. It is a flowchart which shows the flow of the process performed in the electrical storage amount calculation part shown in FIG. It is a graph which shows an example of transition of the electrical storage amount in one day predicted by the prediction method concerning one embodiment of the present invention. It is an example of the prediction result by the prediction method which concerns on one Embodiment of this invention, Comprising: It is the graph which totaled and showed the number of the periods counted as an independence day for every month.

  Hereinafter, embodiments of the present invention will be described with reference to the accompanying drawings. In order to facilitate the understanding of the description, the same constituent elements in the drawings will be denoted by the same reference numerals as much as possible, and redundant description will be omitted.

  The prediction method according to the present embodiment is a method for predicting the transition of the amount of stored electricity when a person has lived for one year in a house including a solar panel as a private power generation device and a power storage device. The power storage device is for temporarily storing power that is not immediately consumed in the house among the power generated by the solar panel. When the amount of power generated by the solar panel falls below the electricity demand of the house (the total power consumed by all the power consuming devices installed in the house, such as air conditioners, TVs, IH cookers, etc.) Power is appropriately supplied to the power consuming device.

  The prediction of the transition of the amount of power storage described below is performed in order to optimize the panel size of the solar panel installed in the house and the capacity of the power storage device. For this reason, the prediction method according to the present embodiment predicts a period (number of days) during which power can be self-sufficient as a guideline when considering the optimization. That is, a simulation is performed under the condition that power supply from the system power supply is not performed, and a period (number of days) in which the user can live without using the power supplied from the system power supply is predicted.

  In the following, the prediction method according to the present embodiment will be described as being performed by a calculation process of a computer including an input device such as a keyboard, an output device such as a display, and a storage device. In other words, it can be said that such a computer is an embodiment of the prediction apparatus according to the present invention.

  FIG. 1 is a block diagram for explaining a prediction method according to the present embodiment. As shown in FIG. 1, in the processing in this prediction method, the generated power amount database 100 and the consumed power amount database 200 are used as a collection of input data, respectively. Based on these input data, a calculation process is performed in the storage amount calculation unit 300 to create a storage amount database 400 that is an aggregate of output data.

  The generated power amount database 100 is a database in which the transition of the power generation amount of the solar panel in one year is stored. In the generated power amount database 100, a year (predetermined period) from midnight on January 1st is divided into unit periods of every hour, and it is predicted that the solar panel generates power for each unit period. The amount of generated power is stored. That is, 24 × 365 generated power amounts are stored in the generated power amount database 100. The generated power amount database 100 is an array variable stored in a storage device of a computer. For this reason, the amount of generated power in one hour from time t is expressed as “generated power amount Ep (t)” below. t is an integer indicating the time elapsed from midnight on January 1 to the time, and takes a value from 0 to (24 × 365-1). Such a generated power amount database 100 is created in consideration of the panel size of the solar panel and the amount of sunlight. A specific creation method will be described later.

  The power consumption database 200 is a database in which changes in the power consumption of a house over a year are stored. The power consumption database 200 divides a year (predetermined period) from midnight on January 1 into unit periods of one hour, and then consumes power installed in a house for each unit period. The power consumption predicted to be consumed by the device is stored. In other words, 24 × 365 pieces of power consumption are stored in the power consumption database 200. The power consumption database 200 is an array variable stored in a computer storage device. For this reason, the power consumption amount for one hour from the time t is expressed as “power consumption amount Ec (t)” below. t is an integer indicating the time elapsed from midnight on January 1 to the time, and takes a value from 0 to (24 × 365-1). Such a power consumption database 200 is created in consideration of the number of consumers, the arrangement of air conditioners, and the like. A specific creation method will be described later.

  The power storage amount database 400 is output by arithmetic processing performed in the power storage amount calculation unit 300, and is a database in which the transition of the power storage amount of the power storage device in one year is stored. In the power storage amount database 400, one year (predetermined period) from midnight on January 1st is divided into unit periods of every hour and then stored in the power storage device at the start of each unit period. Is stored. In other words, 24 × 365 stored power amounts are stored in the stored power amount database 400. The storage amount database 400 is an array variable stored in a storage device of a computer. For this reason, the storage amount at time t is expressed as “storage amount BT (t)” below. t is an integer indicating the time elapsed from midnight on January 1 to the time, and takes a value from 0 to (24 × 365-1). Specific contents of the arithmetic processing performed in the stored electricity amount calculation unit 300 will be described later.

  A specific method for creating the generated power amount database 100 will be described with reference to FIG. FIG. 2 is a block diagram for explaining the flow of processing when the generated power amount database 100 is created. As shown in FIG. 2, in the generation of the generated power amount database 100, the solar panel construction data 110 and the first weather database 120 are used as input data, respectively. Based on these, calculation processing is performed in the generated power amount calculation unit 130, and the generated power amount database 100 is created.

  The solar panel construction data 110 is data input from a computer input device, and is information (specifications) on the power generation performance of a solar panel installed in a house. In this embodiment, a group of data consisting of the panel size of the solar panel, the power value output from the solar panel when irradiated with sunlight, the installation area of the house, the installation direction and the installation angle of the solar panel Is the solar panel construction data 110. The user of the prediction device inputs various types of information about the solar panel to be simulated as the solar panel construction data 110.

  The first weather database 120 is a database in which weather conditions for one year are stored. The first weather database 120 stores weather conditions that affect the power generation amount of the solar panel among various weather conditions. In the present embodiment, a group of data including the position of the sun (the cosine of altitude, the sine of the azimuth angle, the cosine of the azimuth angle), the amount of solar radiation, and the like are employed as the weather condition.

  The first meteorological database 120 stores the above weather conditions at the start of each unit period after dividing the year (predetermined period) from midnight on January 1 into unit periods for each hour. ing. That is, 24 × 365 weather conditions (each of which is a group of data) are stored in the first weather database 120. The first weather database 120 is an array variable stored in a computer storage device.

  Such a first weather database 120 is created from an average value (normal value) based on past weather conditions in an area where a house is installed, and is stored in a storage device. In addition, it is good also as making a user input the area where a house is installed, and selecting and using the suitable 1st weather database 120 based on the said input. In this case, it is necessary to create a plurality of databases based on the normal values of various regions in the country in advance and store them in the storage device.

  The generated power amount calculation unit 130 creates the generated power amount database 100 based on data input from the solar panel construction data 110 and the first weather database 120. Although description about a specific calculation method is omitted, since the transition of the amount of sunshine in the year (the amount of sunshine per hour) is obtained by the first weather database 120, the information included in the solar panel construction data 110 and Based on the change in the amount of sunshine, the change in the amount of power generation in one year, that is, the generated power amount database 100 is easily created. The first weather database also takes into account the amount of sunshine due to weather changes such as rainy weather and cloudy weather.

  Next, a specific method for creating the power consumption database 200 will be described with reference to FIG. FIG. 3 is a block diagram for explaining the flow of processing when the power consumption database 200 is created. As shown in FIG. 3, in the creation of the power consumption database 200, the heating / cooling database 210, the hot water supply database 220, the kitchen database 230, and the home appliance database 240 are used as input data, respectively. Based on these, a calculation process is performed in the power consumption calculation unit 250, and the power consumption database 200 is created.

  The heating / cooling database 210 is a database in which changes in the amount of power consumed by a heating / cooling device such as an air conditioner out of the amount of power consumed in a house are stored. The heating / cooling database 210 divides one year (predetermined period) from midnight on January 1 into unit periods of one hour, and then adds all the heating units installed in the house for each unit period. The amount of power consumption predicted to be consumed by the cooling device is stored. That is, 24 × 365 pieces of power consumption are stored in the heating / cooling database 210. The heating / cooling database 210 is an array variable stored in a storage device of a computer. Such a heating / cooling database 210 is created in consideration of the layout of a house, the arrangement of air conditioners in each room, and the like. A specific creation method will be described later.

  The hot water supply database 220 is a database in which changes in the amount of electric power consumed by a hot water supply device such as an electric water heater among the electric power consumption in a house are stored. The hot water supply database 220 divides a year (predetermined period) from midnight on January 1 into unit periods of every hour, and then, for each unit period, all the water heaters installed in the house The amount of power consumed that is expected to be consumed is stored. That is, 24 × 365 power consumption amounts are stored in the hot water supply database 220. The hot water supply database 220 is an array variable stored in a storage device of a computer. Such a hot water supply database 220 is created in consideration of the type and frequency of use of hot water supply equipment installed in a house. A specific creation method will be described later.

  The kitchen database 230 is a database in which the transition of the amount of power consumed by electrical appliances for kitchens such as an IH cooker is stored among the amount of power consumed in a house. In the kitchen database 230, one year (predetermined period) from midnight on January 1st is divided into hourly unit periods, and for each unit period, all the kitchens installed in the house The amount of power consumed that is expected to be consumed by electrical equipment is stored. That is, 24 × 365 pieces of power consumption are stored in the kitchen database 230. The kitchen database 230 is an array variable stored in a storage device of a computer. Such a kitchen database 230 is created in consideration of the type and frequency of use of kitchen electrical equipment installed in a house. A specific creation method will be described later.

  The home appliance database 240 is a database in which changes in the amount of power consumed by home appliances such as a television among the power consumption in a house are stored. The “home appliance” here does not include heating / cooling equipment such as an air conditioner or kitchen electrical equipment such as an IH cooker. The household appliance database 240 divides the year (predetermined period) from midnight on January 1 into unit periods of every hour, and then all household appliances installed in the house for each unit period. The amount of power consumed that is expected to be consumed is stored. That is, 24 × 365 pieces of power consumption are stored in the home appliance database 240. The home appliance database 240 is an array variable stored in a storage device of a computer. Such home appliance database 240 is created in consideration of the type and frequency of use of home appliances installed in a house. A specific creation method will be described later.

  The power consumption amount calculation unit 250 creates the power consumption amount database 200 based on data input from the heating / cooling database 210, the hot water supply database 220, the kitchen database 230, and the home appliance database 240, respectively. The power consumption amount calculation unit 250 in the present embodiment creates the power consumption amount database 200 by calculating the total value of the power consumption amounts input from the four databases for each unit period.

  A specific method for creating the heating / cooling database 210 will be described with reference to FIG. FIG. 4 is a block diagram for explaining the flow of processing when the heating / cooling database 210 is created. As shown in FIG. 4, in the creation of the heating / cooling database 210, the housing CAD data 211, the housing construction site data 212, the consumer data 213, the heat insulation data 214, and the second weather database 215, Each is used as input data. Based on these, calculation processing is performed in the heating / cooling load calculation unit 216, and the load of the heating / cooling device, that is, the transition of energy to be output by all the heating / cooling devices installed in the house (value per unit period). Is calculated.

  The residential CAD data 211 is CAD data created at the time of designing a house, and includes structural information such as the size and floor plan of the house. The housing construction area data 212 is information on the land where the house is constructed, and includes the name of the area where the land exists (for example, “Tokyo”) and the direction of the land (for example, “slope facing south”). It is data to include. In the present embodiment, the direction (for example, which room is facing south) when a house is constructed on the land is also included in the house construction area data 212.

  The consumer data 213 is data including the number of people living in a house, each gender, occupation, age composition, and the like. The consumer data 213 also includes information on whether each person living in a house is expected to be in each room for each unit period or whether he / she is out and absent (information on lifestyle). Such consumer data is, for example, “The whole family often spends their time in the living room around 20:00 on holidays” or “From midnight to 6:00 am, they often spend their time in their bedrooms. It may be set based on a general tendency such as “ In this case, it is possible to automatically set the lifestyle of each consumer based on the input information such as the family structure. Of course, instead of automatically setting based on a general tendency, the user may input freely when making a prediction, and the circumstances of each household may be reflected.

  The lifestyle included in the consumer data 213 is not limited to the above example. For example, it may affect the use of heating and cooling equipment, such as the tendency to not use air conditioners due to high environmental awareness, or the need to keep the room at a comfortable temperature during treatment. Various kinds of information can be included.

  The heat insulation data 214 is data relating to the heat insulation performance of the heat insulating material disposed on the outer wall of the house. The heat insulating data 214 includes information on the material of the heat insulating material arranged on each wall, the thermal conductivity, the heat capacity, and the like. The heat insulation data 214 may be data automatically set based on a public standard such as a next generation energy saving standard. Moreover, it is good also as a part of information contained in the housing CAD data 211. With the heat insulation data 214, the heat insulation performance (ease of heat entering and exiting each room) is set for each room of the house set by the house CAD data 211.

  The second weather database 215 is a database in which weather conditions for one year are stored. The second weather database 215 stores weather conditions that affect the heating / cooling load of the house among various weather conditions. In the present embodiment, a group of data including temperature, humidity, and amount of sunlight is adopted as the weather condition. The amount of sunshine affects the heating / cooling load as energy reaching the room through the window. For example, the load (energy) required for heating is reduced during the winter when the sun is relatively strong.

  The second weather database 215 stores the above weather conditions at the start of each unit period after dividing the year (predetermined period) from midnight on January 1 into unit periods of every hour. ing. That is, 24 × 365 weather conditions (each of which is a group of data) are stored in the second weather database 215. The second weather database 215 is an array variable stored in a computer storage device.

  Such a second weather database 215 is created from an average value (normal value) based on past weather conditions in an area where a house is installed, and is stored in a storage device. In addition, it is good also as making a user input the area where a house is installed, and selecting and using the suitable 2nd weather database 215 based on the said input. In this case, it is necessary to create a plurality of databases based on the normal values of various regions in the country in advance and store them in the storage device.

  In the heating / cooling load calculation unit 216, the load of the heating / cooling device is based on data input from the housing CAD data 211, the housing construction site data 212, the consumer data 213, the heat insulation data 214, and the second weather database 215. , That is, the transition of energy to be output by the heating / cooling device in the house (the energy to be output in each unit period, where the unit is kW).

  An example of a specific calculation method is shown. First, the heating / cooling load calculation unit 216 calculates the transition of the heating / cooling load that is expected to be required in a house for each unit period and for each room. In calculating such a transition of the heating / cooling load, for example, it is considered that the temperature of the room rises due to heat generation of the human body during a time zone in which a person is present in the room. In addition, it is considered that the temperature of the room rises in a time zone in which sunlight enters the room through the window. In addition, it is considered that the transition of the outside air temperature, the heat insulation performance of the wall, and the like affect the heat flow between the outdoors and the room and the heat flow between the rooms. Furthermore, even if the room becomes uncomfortable temperature, it is considered that the heating / cooling load required for the heating / cooling device in the room is zero in a time zone when no person is present in the room.

  The transition of the heating / cooling load required in each room is converted into the transition of the electric energy consumed by the heating / cooling device in each room in the heating / cooling electric energy calculation unit 218. For the conversion, heating / cooling equipment data 217 is used.

  The heating / cooling device data 217 is a collection of data including the arrangement of all the heating / cooling devices installed in the house (the presence / absence and number of installed heating / cooling devices in each room) and specifications (heating / cooling performance). It includes the rated output and COP of each heating and cooling device. COP (Coefficient Of Performance) is a value indicating energy output by the heating / cooling device per 1 kW of power consumption.

  The heating / cooling electric energy calculation unit 218 divides the transition of the heating / cooling load calculated by the heating / cooling load calculation unit 216 by the COP of the heating / cooling device installed in each room, whereby each heating / cooling device Convert to the transition of power consumption. The heating / cooling power amount calculation unit 218 further multiplies the value converted above by the length of the unit period, thereby converting the value into a change in the amount of power consumed for each unit period (unit: kWh).

  Thereafter, the heating / cooling power amount calculation unit 218 obtains the transition of the power amount predicted to be consumed by all the heating / cooling devices installed in the house by summing the transition of the power amount for each unit period. . That is, the heating / cooling database 210 is created.

  Next, a specific method for creating the hot water database 220 will be described with reference to FIG. FIG. 5 is a block diagram for explaining the flow of processing when the hot water supply database 220 is created. As shown in FIG. 5, in the creation of the hot water supply database 220, the consumer data 221 is used as input data. Based on the input data, the hot water supply pattern calculation unit 222 performs arithmetic processing, and the load of the hot water supply equipment, that is, the transition of energy to be output by all the hot water supply equipment (electric water heaters, etc.) installed in the house (unit: Value for each period) is calculated.

  The consumer data 221 is data including the number, sex, occupation, age composition, etc. of people living in a house. The consumer data 221 also includes information on lifestyle, such as whether each person living in a house is expected to be in each room for each unit period, or whether he / she is away from home. . Such consumer data is, for example, “The whole family often spends their time in the living room around 20:00 on holidays” or “From midnight to 6:00 am, they often spend their time in their bedrooms. It may be set based on a general tendency such as “ In this case, it is possible to automatically set the lifestyle of each consumer based on the input information such as the family structure. Of course, instead of automatically setting based on a general tendency, the user may input freely when making a prediction, and the circumstances of each household may be reflected. The consumer data 221 may be data common to the consumer data 213 used when the heating / cooling database 210 is created.

  In the hot water supply pattern calculation unit 222, based on the input consumer data 221, the change in the load of the hot water supply device, that is, the change in the energy that the hot water supply device (electric water heater, etc.) should output in the house (in each unit period) Energy to be output, the unit here being kW).

  An example of a specific calculation method is shown. First, the hot water supply pattern calculation unit 222 calculates the transition of the amount of hot water required in the house based on the consumer data 221. That is, for each unit period, the amount of hot water (unit: liter) required for the period is calculated. In calculating such a required amount of hot water, it is considered that, for example, in the morning time zone, an amount of hot water proportional to the number of consumers is used in the washstand. In the evening, a large amount of hot water is used in the bathroom. Furthermore, it is also considered that the time zone in which the bathroom is used varies depending on the return time of the consumer.

  The change in the amount of hot water required in the house is converted into the change in the amount of power consumed by the hot water supply device in the house in the hot water supply power amount calculation unit 224. For the conversion, hot water supply device data 223 is used.

  The hot water supply device data 223 is a collection of data including the arrangement and specifications (heating performance) of all the hot water supply devices installed in the house, and includes the APF of each hot water supply device. APF (Annual Performance Factor of hot water supply) is also called annual hot water supply efficiency, and indicates the energy output by the hot water supply device per 1 kW of power consumption when the hot water supply device is operated under certain conditions. Value.

  The hot water supply power amount calculation unit 224 divides the change in hot water supply amount calculated by the hot water supply pattern calculation unit 222 by the APF of the hot water supply device installed in the house, thereby changing the power consumed by each hot water supply device. Convert. The hot water supply power amount calculation unit 224 further multiplies the value converted above by the length of the unit period to convert the amount of power consumed per unit period (unit: kWh). In calculating the transition of electric power consumed by the hot water supply equipment, it is not calculated based only on the amount of hot water required, but considering that the temperature of the hot water varies depending on the scene in which the hot water is used. Also good.

  Thereafter, the hot water supply power amount calculation unit 224 obtains the transition of the amount of power predicted to be consumed by all the hot water supply devices installed in the house by summing the transition of the power amount for each unit period. That is, a hot water supply database 220 is created.

  Next, a specific method for creating the kitchen database 230 will be described with reference to FIG. FIG. 6 is a block diagram for explaining the flow of processing when creating the kitchen database 230. As shown in FIG. 6, in creating the kitchen database 230, the consumer data 231 is used as input data. Based on the input data, a calculation process is performed in the kitchen usage pattern calculation unit 232, and the load of the electrical appliance for kitchen, that is, all the electrical appliances for kitchen (IH cooker, etc.) installed in the kitchen of the house are output. The energy transition (value per unit period) to be calculated is calculated.

  The consumer data 231 is data including the number of people living in a house, gender, occupation, age structure, and the like. In particular, it includes data on the frequency of use of the kitchen and the time period in which it is used, such as the presence or absence of a full-time housewife and the habit of having breakfast. The consumer data 231 may be data common to the consumer data 213 used when the heating / cooling database 210 is created.

  In the kitchen usage pattern calculation unit 232, based on the input consumer data 231, the transition of the load on the kitchen electrical device, that is, the transition of the energy to be output by the kitchen electrical device (IH cooker, etc.) in the kitchen ( Energy to be output in each unit period, where the unit is kW).

  An example of a specific calculation method is shown. First, the kitchen usage pattern calculation unit 232 calculates a change in the amount of heat necessary for the kitchen based on the consumer data 231. That is, for each unit period, the amount of heat (unit: kilojoule) required for the period is calculated. In calculating the transition of the required heat amount, for example, it is considered that a large amount of heat is required by using an IH cooker or the like in a morning or evening time zone in a home with a full-time housewife. It is also considered that the amount of heat required in the kitchen increases in proportion to the number of people living at home. In addition, it is considered that the amount of heat required in the kitchen is small in all time zones at homes where there are many restaurants. Furthermore, it is considered that the amount of heat required in the morning time zone is relatively small in a household where there is no habit of having breakfast.

  The change in the amount of heat required in the kitchen is converted into a change in the amount of electric power consumed by the electric appliance for the kitchen in the kitchen electric energy calculation unit 234. For the conversion, kitchen device data 233 is used.

  The kitchen device data 233 is a collection of data including the arrangement and specifications (heating performance) of all the kitchen electrical devices installed in the kitchen, and includes the heating efficiency of each kitchen electrical device. The heating efficiency is a value indicating the ratio between the amount of heat output from the kitchen electrical device and the power consumption.

  In the kitchen electric energy calculation unit 234, the change in the amount of heat calculated by the kitchen usage pattern calculation unit 232 is divided by the heating efficiency of the kitchen electrical device installed in the kitchen, thereby being consumed by each kitchen electrical device. It is converted into the transition of the amount of electric power (unit: kWh).

  Thereafter, the kitchen power amount calculation unit 234 obtains the transition of the power amount that is predicted to be consumed by all kitchen electrical devices installed in the kitchen by summing the transition of the power amount for each unit period. . That is, the kitchen database 230 is created.

  Next, a specific method for creating the home appliance database 240 will be described with reference to FIG. FIG. 7 is a block diagram for explaining the flow of processing when creating the home appliance database 240. As shown in FIG. 7, in creating the home appliance database 240, the consumer data 241 is used as input data. Based on the input data, a calculation process is performed in the home appliance usage pattern calculation unit 242 to predict the transition of the usage state (for example, ON or OFF) of each home appliance.

  The consumer data 241 is data including the number of people living in a house, gender, occupation, age structure, and the like. The consumer data 241 also includes information on whether each person living in a house is expected to be in each room for each unit period or whether he / she is out and absent (information on lifestyle). Such consumer data may be set on the basis of a general tendency such as “at around 20:00 on holidays, the whole family often spends watching TV in the living room”. . In this case, it is possible to automatically set the lifestyle of each consumer based on the input information such as the family structure. Of course, instead of automatically setting based on a general tendency, the user may input freely when making a prediction, and the circumstances of each household may be reflected. The consumer data 241 may be data common to the consumer data 213 used when the heating / cooling database 210 is created.

  The household appliance usage pattern calculation unit 242 predicts the transition of the usage state of each household electrical appliance based on the input consumer data 241. In the present embodiment, the predicted usage time of each home appliance (such as a television) installed in a house is calculated for each unit period.

  In predicting the transition of the usage state of each home appliance, for example, in a home with a full-time housewife, it is considered that the frequency of use of a vacuum cleaner or a washing machine increases during the daytime.

  The transition of the usage state of each home appliance is converted into the transition of the amount of power consumed by each home appliance in the home appliance power amount calculation unit 244. The home appliance data 243 is used for the conversion. The home appliance data 243 is information about all home appliances installed in the home, and is a collection of data including each type, arrangement, rated power consumption, and the like.

  Based on the transition of the usage state of each home appliance calculated by the home appliance usage pattern calculation unit 242, the home appliance power amount calculation unit 244 calculates the amount of power that each home appliance is expected to consume in each unit period. . Specifically, by multiplying the predicted usage time of each home appliance in the unit period by the rated power consumption of the home appliance, the amount of power (unit: kWh) predicted to be consumed by the home appliance in the unit period is Calculated.

  Thereafter, the home appliance power amount calculation unit 244 obtains the transition of the amount of power predicted to be consumed by all home appliances installed in the house by summing the transition of the power amount for each unit period. That is, the home appliance database 240 is created.

  Next, with reference to FIG. 8, the content of specific calculation processing performed in the storage amount calculation unit 300 shown in FIG. 1 will be described. FIG. 8 shows a flow of processing performed by the power storage amount calculation unit 300, that is, a specific processing flow for creating the power storage amount database 400 based on the generated power amount database 100 and the power consumption amount database 200. It is a flowchart to show. This processing is performed inside a computer that is a prediction device.

  As already described, in the prediction method according to the present embodiment, the year from midnight on January 1st is divided into unit periods of every hour, and then the storage device is stored at the start of each unit period. The amount of electricity stored that is predicted to be stored is calculated. That is, 24 × 365 elements stored in the storage amount BT (t), which is an array variable, are calculated.

  First, in step S01, 0, which is an initial value for each of a plurality of variables, is stored. These variables include continuous independence time Jh, independence days Jc, power storage amount buffer BT, and time t.

  The continuous independence time Jh and the independence days Jc are variables for storing a count value added when a predetermined condition is satisfied in the process of performing the process shown in FIG. The meaning of each condition and count value will be described later.

  As described above, the time t is an integer representing the time elapsed from midnight on January 1 and takes a value from 0 to (24 × 365-1). The process shown in FIG. 8 sequentially calculates values stored in each element of the storage amount BT (t) while increasing the value of time t from 0 by 1. That is, the unit period starting from time t corresponds to the “prediction target period” of the present invention.

  The storage amount buffer BT is a buffer variable that is used to temporarily store the value when calculating the value of the storage amount BT (t).

  In step S02 following step S01, the value of the generated power amount corresponding to time t is calculated and stored in the generated power amount Ep (t). Similarly, the value of the power consumption corresponding to time t is calculated and stored in the power consumption Ec (t). Further, the value stored in the storage amount buffer BT is stored in the storage amount BT (t) as the storage amount of the storage device at time t.

  Note that the value of the amount of generated power and the value of the power consumption may be calculated in advance for all times t. That is, the generation of the power generation amount database 100 and the generation of the power consumption amount database 200 may be completed at a stage prior to the start of the series of processes shown in FIG. In this case, in step S02, the value of the generated power amount at time t is read from the generated power amount database 100, and the value is used as the generated power amount Ep (t). Similarly, the value of the power consumption at time t is read from the power consumption database 200 and used as the power consumption Ec (t).

  In step S03 following step S02, the value of the generated power amount Ep (t) and the value of the power consumption amount Ec (t) are compared. When the value of the generated power amount Ep (t) is larger, the generated power amount in the unit period starting at time t is larger than the consumed power amount in the unit period. For this reason, in the unit period starting at time t, surplus power is stored in the power storage device. In this case, the process proceeds to step S04, and 1 is added to the continuous independent time Jh.

  The continuous self-sustained time Jh is obtained by adding 1 when the amount of stored electricity at the end of the prediction target period is calculated to be greater than 0. After that, when the unit period following the prediction target period (the unit period starting at the time t after the time t is increased by 1) is set as the next prediction target period, the storage amount at the end of the prediction target period is When the value is again calculated to be greater than 0, 1 is further added to the continuous free standing time Jh. On the other hand, when the storage amount at the end of the prediction target period is calculated as 0, the value of the continuous self-supporting time Jh is reset to 0.

  In step S05 following step S04, a value obtained by subtracting the power consumption amount Ec (t) from the generated power amount Ep (t) is added to the power storage amount buffer BT. The value of the power storage amount buffer BT corresponds to the power storage amount of the power storage device at the end of the unit period (prediction target period) starting at time t.

  However, the power storage device cannot store the amount of power exceeding the maximum power storage amount BTlim. For this reason, in step S06 and step S07 following step S05, correction is performed in consideration of this point. Note that the value of the maximum charged amount BTlim is preset by the user of the prediction device.

  In step S06, the value of the charged amount buffer BT is compared with the maximum charged amount BTlim. If the value of the storage amount buffer BT is larger, the process proceeds to step S07, and the maximum storage amount BTlim is stored as the value of the storage amount buffer BT. When the value of the storage amount buffer BT is equal to or less than the maximum storage amount BTlim, the process of step S07 is skipped.

  In a succeeding step S08, it is determined whether or not the value of the continuous independent time Jh is smaller than 24. When the value of the continuous free standing time Jh is smaller than 24, the process proceeds to step S09. On the other hand, when the value of the continuous independent time Jh becomes 24, the process proceeds to step S10.

  A value of 24 for the continuous independence time Jh means that a plurality of unit periods (hereinafter, such unit periods are also referred to as independence unit periods) calculated that the amount of stored electricity at each end time is greater than 0 are 24 Means continuous over time. At this time, in step S10, 1 is added to the number of independent days Jc. The number of days of independence Jc is a value indicating how many days the independence day, which is the entire period in which the independence unit period continues for 24 hours, exists in the year from midnight on January 1st. is there. In step S10, after 1 is added to the number of independent days Jc, the value of the continuous independent time Jh is reset to 0.

  In a succeeding step S09, it is determined whether or not the value of the time t is equal to 24 × 365. When the value of time t is equal to 24 × 365, it means that all elements of the storage amount BT (t), which is an array variable, have been calculated. That is, the calculation of all the storage amounts at the end of each unit period was made while going through each unit period of 24 × 365 as the target period in order from the start to the end of the predetermined period. That is. Therefore, in this case, the process ends.

  On the other hand, when the value of time t is not 24 × 365 in step S09, that is, when the value is smaller than 24 × 365, 1 is added to time t in subsequent step S11. That is, the next unit period following the current prediction target period is set as a new prediction target period. Thereafter, the processes after step S02 are repeated.

  In step S03, when the value of the generated power amount Ep (t) is equal to or less than the value of the power consumption amount Ec (t), the process proceeds to step S20. In step S20, the magnitude of the value of the stored electricity amount buffer BT is compared with the magnitude of the value obtained by subtracting the generated power amount Ep (t) from the consumed power amount Ec (t). Here, the value obtained by subtracting the power generation amount Ep (t) from the power consumption amount Ec (t) corresponds to a decrease in the amount of power storage in the prediction target period.

  If the value of the power storage amount buffer BT is larger in step S20, this means that the power storage amount at the end of the prediction target period is greater than zero. In this case, the process proceeds to step S21, and the decrease in the charged amount is subtracted from the charged amount buffer BT. Thereafter, in the subsequent step S22, 1 is added to the continuous free standing time Jh, and then the process proceeds to step S08.

  In step S20, if the value of the storage amount buffer BT is equal to or less than the decrease in the storage amount, in other words, the difference obtained by subtracting the power consumption amount Ec (t) from the generated power amount Ep (t) is calculated as the storage amount buffer BT. If the value added to the value of 0 is 0 or a negative value, the process proceeds to step S30. In this case, since the storage amount at the end of the prediction target period is 0, the value of the storage amount buffer BT is set to 0 in step S30. Further, the value of the continuous free standing time Jh is reset to 0 (step S31). Thereafter, the process proceeds to step S08.

  By repeating the processing described above until the value of time t becomes equal to 24 × 365, all elements of the storage amount BT (t), which is an array variable, are calculated. That is, the transition of the charged amount during a predetermined period is calculated (predicted).

  In the present embodiment, the time t is incremented by 1 as described above, and each element of the storage amount BT (t) is calculated in order, and in parallel therewith, the number of consecutive independent unit periods is counted. (Steps S04, S22, S31: first counting step). Similarly, the determination whether or not the continuous independent unit period is an independent day and the count of the independent day are performed in parallel with the calculation of each element of the storage amount BT (t) (steps S08 and S10). : Second counting step). However, the embodiment of the present invention need not be limited to such an embodiment. For example, after all the calculation of each element of the stored electricity amount BT (t) is completed, the count of the continuous number of independent unit periods and the count of independent days may be collectively performed.

  As is clear from the above description made with reference to FIG. 8, in the present embodiment, the start of the independent day is not fixed at a specific time in the day (for example, midnight). That is, for example, the fact that the amount of stored electricity has never been calculated as 0 between midnight and midnight the next day is not a condition for counting the period as an independent day. If 1 is set in Jh in step S04, the time t at that time can be the start of an independent day.

  If the start date of the independent day is fixed at, for example, midnight, the condition for counting as an independent day becomes stricter than the condition in the present embodiment. For example, even if the amount of stored power is not calculated as 0 from 1:00 am on one day to 11 pm on the next day (the length of the period is 46 hours), From 1 am to 1 am, and the next day from 11 pm to 12:00 pm (midnight), the independence day is not included in the period. End up. However, in view of the fact that the count value for an independent day is one measure when considering how much self-sufficiency of power can be achieved within a predetermined period, such a method for counting an independent day is not appropriate. . In fact, the peak time of power consumption in a house (the time of the day) is not always constant, and it can vary from day to day or every certain period (for example, every season). It is difficult to reflect the actual situation without fixing the time at a specific time in the day.

  In this embodiment, as described above, the start of the independent day is not fixed at a specific time in the day, so that the count of the independent day, that is, the time when the amount of power stored in the power storage device does not become zero continues for 24 hours. Appropriate period counting. As a result, the obtained count value can be used as an appropriate standard when examining how much power self-sufficiency can be realized.

An example of the prediction result obtained by the prediction method according to the present embodiment is shown in FIG. FIG. 9A shows the transition of the amount of power stored in a day on May 14 among the prediction results. FIG. 9B shows the transition of the amount of power stored in a day on December 14 among the prediction results. A graph 501 is a transition of the generated power of the solar panel, and a graph 502 is a transition of the amount of power stored in the power storage device. A graph 503 is a transition of power consumed in the house. The above-mentioned assumption is that the house is a two-story detached house 120m ² located in Tokyo (document “Housing business owner's judgment criteria”, self-sustained recycling house model). The case where the optical panel 8kW and the storage battery 18kWh were installed was assumed. Furthermore, the above prediction was made assuming that the residents of the house are a family of four (a couple, one elementary school student, and one high school student).

  As shown in FIG. 9 (A), in May, when the sunlight was relatively strong, power was generated with a sufficient amount of power using the solar panel. For this reason, on the day of May 14 (24 hours), the amount of stored electricity never became zero. That is, May 14 was counted as an independent day.

  On the other hand, as shown in FIG. 9B, the amount of power generated by the solar panel was not sufficient in December when the sunlight was relatively weak. For this reason, between 2 o'clock and 5 o'clock, the amount of electricity stored in the electricity storage device has become zero. That is, December 14 was not counted as an independent day.

  As described above, according to the prediction method according to the present embodiment, it is possible to predict the transition of the amount of power stored in the power storage device while taking into account various conditions that differ for each house, such as the structure of the house and the lifestyle of the consumer. it can. For this reason, it becomes possible to optimize the panel size of a solar panel, the capacity | capacitance of an electrical storage apparatus, etc. based on the said prediction.

  Another example of the prediction result obtained by the prediction method according to this embodiment is shown in FIG. FIG. 10 is a bar graph in which the number of periods counted as independence days is tabulated for each month. As shown in FIG. 10, in the summer season (May to August) with relatively strong sunlight, it can be seen that 20 days or more of one month were counted as independent days. On the other hand, it can be seen that during the winter season (December to February) when the sunlight is relatively weak, no day was counted as an independent day. Based on such a result, the user of the prediction device according to the present embodiment can examine optimization of the panel size of the solar panel and the capacity of the power storage device.

  The embodiments of the present invention have been described above with reference to specific examples. However, the present invention is not limited to these specific examples. In other words, those specific examples that have been appropriately modified by those skilled in the art are also included in the scope of the present invention as long as they have the characteristics of the present invention. For example, the elements included in each of the specific examples described above and their arrangement, materials, conditions, shapes, sizes, and the like are not limited to those illustrated, but can be changed as appropriate. Moreover, each element with which each embodiment mentioned above is provided can be combined as long as technically possible, and the combination of these is also included in the scope of the present invention as long as it includes the features of the present invention.

100: Power generation database 110: Solar panel construction data 120: First weather database 130: Power generation calculation unit 200: Power consumption database 210: Heating / cooling database 211: Housing CAD data 212: Housing construction site data 213: Consumer data 214: Thermal insulation data 215: Second weather database 216: Heating / cooling load calculation unit 217: Heating / cooling equipment data 218: Heating / cooling electric energy calculation unit 220: Hot water supply database 221: Consumer data 222: Hot water supply pattern calculation unit 223: Hot water supply device data 224: Hot water supply electric energy calculation unit 230: Kitchen database 231: Consumer data 232: Kitchen usage pattern calculation unit 233: Kitchen device data 234: Kitchen electric energy calculation unit 240: Home appliance database 241: Consumer data 242 :House Using pattern calculation section 243: Appliances Data 244: home appliance power calculation means 250: power consumption calculation unit 300: power storage amount calculation unit 400: power storage quantity database

Claims (8)

A private power generator,
A power consuming device that consumes at least a portion of the power generated by the private power generator, and
The electric power that is not consumed by the power consuming device out of the electric power generated by the private power generator is temporarily stored, and when the electric power generated by the private power generator is insufficient, the electric power is supplied to the power consuming device. A power storage device for supplying
Is a method of predicting the transition of the amount of power stored in the power storage device in a predetermined period under the condition that power supply from a system power supply is not performed,
One of the predetermined periods divided into a plurality of unit periods is set as a prediction target period, and a first step of calculating a power generation amount predicted to be generated by the private power generation apparatus in the prediction target period;
A second step of calculating an amount of power consumption predicted to be consumed by the power consuming device in the prediction target period;
A value obtained by adding a difference obtained by subtracting the power consumption amount from the generated power amount to the power storage amount at the start time of the prediction target period is calculated as the power storage amount at the end time of the prediction target period. And a third step to
All the unit periods are sequentially used as the prediction target period from the start to the end of the predetermined period, and the first process, the second process, and the third process are repeated for each of the unit periods. Predicting the transition of the amount of electricity stored,
In the third step, when the value obtained by adding the difference exceeds the maximum power storage amount of the power storage device, the power storage amount is calculated as the same value as the maximum power storage amount, and the difference is added. When the value obtained in this way becomes a negative value, the storage amount is calculated as 0. When the unit period in which the amount of electricity calculated in the third step is a value other than 0 is defined as an independent unit period,
The prediction method according to claim 1, further comprising a first counting step of measuring a continuous number of the independent unit periods. When the entire period in which the plurality of independent unit periods are continuous over 24 hours is defined as an independent day,
The prediction method according to claim 2, further comprising a second counting step of measuring the number of independent days included in the predetermined period. The in-house power generator varies the amount of power generation depending on the surrounding weather conditions,
A power generation performance setting step in which the power generation performance of the private power generator is set;
As a weather condition that affects the amount of power generated by the private power generation device, the first weather setting step in which the first weather condition is set, and further,
4. The prediction method according to claim 1, wherein, in the first step, the generated power amount is calculated based on the set power generation performance and the first weather condition. 5. A floor plan setting step for setting the floor plan of the house;
A heat insulation performance setting step for setting the heat insulation performance of the house;
A number of people setting process for setting the number of people living in the house;
A heating / cooling device setting step for setting heating / cooling device information, including the number of installed heating / cooling devices in the house, an installation location, and heating / cooling performance,
A second weather setting step in which a second weather condition is set as a weather condition that affects the heating and cooling load of the house,
The power consumption is calculated based on the set floor plan, the heat insulation performance, the number of persons, the heating / cooling device information, and the second weather condition in the second step. The prediction method as described in any one of thru | or 4. Further comprising a lifestyle setting step for setting a lifestyle of a person living in the house,
In the second step, the power consumption is calculated based on the set floor plan, the heat insulation performance, the number of persons, the heating / cooling device information, the second weather condition, and the lifestyle. The prediction method according to claim 5.   A prediction device that predicts a change in the amount of power stored in the power storage device by the prediction method according to claim 1 and notifies the user of the prediction result.   A prediction device that predicts a change in the amount of electricity stored in the electricity storage device by the prediction method according to claim 3 and notifies the user of the number of independent days measured by the second counting step.

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