JP2005265201A - Hot water heating load prediction system - Google Patents

Abstract

<P>PROBLEM TO BE SOLVED: To provide a hot water heating load prediction system for predicting a hot water use condition of hot water heating equipment with high precision by taking changes of meteorological situation into account. <P>SOLUTION: This system is provided with an operation information inputting part 1 for receiving inputting of operation information related to a hot water use condition of the hot water heating equipment 31, a meteorological observation data inputting part 2 for receiving inputting of one or a plurality of types of predetermined meteorological observation data, an operation information data base preparation part 3 for making the operation information and the meteorological observation data correspond mutually every time when receiving respective inputting and storing them in an operation information data base 6, a meteorological forecast data inputting part 4 for receiving a predetermined forecast value of the meteorological observation data, and a thermal load prediction part 5 for predicting a hot water use condition of the hot water heating equipment 31 from the corresponding relation of the operation information and the meteorological observation data stored in the operation information data base 6 based on the forecast value. <P>COPYRIGHT: (C)2005,JPO&NCIPI

Description

  The present invention relates to a hot water heating load prediction system that predicts the hot water usage state of a hot water heating device, and is particularly suitable for hot water usage state prediction of a hot water heating device that uses hot water generated by exhaust heat recovery of a cogeneration system as a heat source. The present invention relates to a hot water heating load prediction system.

Conventionally, energy consumed in ordinary households is supplied in the form of electric power, city gas, etc. from an electric power company or a gas company and is individually consumed. By the way, recently, a distributed energy system aimed at reducing CO ₂ emissions and saving energy has been actively developed and put into practical use, and power is generated in a power consuming area even in ordinary homes, apartment houses, offices, etc. The use of distributed power generation systems is expected to progress rapidly in the future. In particular, a gas engine cogeneration system that can supply both heat and electric power is attracting attention because of its high energy efficiency because it can effectively use not only electric power but also thermal energy generated by the gas engine at the same time. As a result of the diversification of energy supply forms in general households, there is room for greatly improving environmental performance such as energy costs and CO ₂ emissions due to the consistency between household energy demand trends and energy supply forms. Occurred.

  Therefore, in the past, power supply for electric loads such as electrical equipment was exclusively provided by electric power from commercial power systems such as electric power companies, but by introducing a home cogeneration system and connecting to the commercial power system, In the case of a usage mode in which the shortage of power generated by the cogeneration system is compensated with the power supply from the commercial power system, the cogeneration system can be operated efficiently according to the domestic power demand. Therefore, it has been proposed to perform an operation plan in accordance with the power demand and operate the cogeneration apparatus based on the operation plan (see, for example, Patent Document 1 below).

  By the way, since the cogeneration apparatus can also supply heat energy by using exhaust heat generated with power generation, it is possible to further improve the energy utilization efficiency by effectively using the heat energy. it can. Therefore, in order to efficiently use the cogeneration system, power is generated according to the power demand and heat demand, and when the exhaust heat is used, the generated power is completely consumed and the exhaust heat is used. It is important to completely consume the heat energy (see, for example, Patent Document 2 below).

  Therefore, in the operation control of the cogeneration apparatus, it is possible to operate the cogeneration apparatus efficiently by using not only the power demand prediction but also the heat demand prediction.

In the conventional heat demand forecast, the actual value of the heat load (heat consumption) for each time zone (for example, every hour) in the past (for example, the same day on the previous day, the day before the previous day, the same day of the week before) Based on the past performance data, the heat load for each time zone of the day was predicted according to a certain calculation rule. For example, it is conceivable to create a linear regression equation using past actual data of a plurality of days as an explanatory variable and the heat load of the day as an objective variable, and to predict using the primary regression equation.
JP 2003-209994 A JP 2002-138902 A

  However, since the hot water use state (on / off of the device or the amount of hot water used) of the hot water heater greatly fluctuates depending on weather conditions, the above-described conventional heat demand prediction has a high possibility of being greatly deviated. On the other hand, even if the heat demand is the same, in the case of hot water supply demand (demand to use hot water from a hot water tap such as a hot water bath in a bathtub), hot water is used in a constant demand pattern regardless of the weather conditions. Therefore, the correction can be made in the case of an increase or decrease in the amount of heat consumed due to weather conditions, in particular, the temperature or water temperature.

  As an example of a hot water heater, when a hot water floor heating system is assumed, for example, when the temperature suddenly drops, the user starts the floor heating operation. A load will be generated. Or the operation time zone of floor heating expands or the amount of heat consumption per unit time increases. On the other hand, when the temperature suddenly rises, the user stops the floor heating operation, so that the heat load in the same time zone in the past is not generated. Alternatively, the floor heating operating time period is reduced, or the amount of heat consumed per unit time is reduced. As a result, there is a problem that immediately after the temperature changes rapidly, an error between the heat load prediction based on the past heat load performance data in the same time zone and the actual heat load becomes large.

  As another example of the hot water heater, when a bathroom dryer is assumed, it is used not only for drying purposes but also for heating a bathroom in winter, but it is exclusively used for drying laundry except in winter. However, the use of bathroom dryers other than in winter is also considered to be used in such a way that when the weather is fine, the laundry is dried outdoors and the bathroom dryer is operated and dried in the bathroom only in rainy weather. Since the state greatly depends on the weather conditions, it is difficult to predict the heat load of a bathroom dryer by conventional heat demand prediction.

  In household cogeneration systems that are currently in widespread use, electricity demand forecasts by time zone so that the amount of heat (hot water temperature and hot water volume) required by the hot water supply demand time is stored in the hot water storage tank (heat storage) In combination with the result of the above, the control of operating the generator of the cogeneration apparatus is performed in a time zone in which the overall energy efficiency is judged to be high.

  In addition, when the operation of the hot water heater starts during the operation of the generator, a part of the amount of heat obtained by the exhaust heat recovery is supplied to the hot water heating load via the heat exchanger for heating. When the heating load exceeds the prediction, the amount of stored hot water becomes insufficient, and it is necessary to heat with a backup boiler (auxiliary heat source) when supplying hot water to the hot water supply load, resulting in a decrease in energy efficiency. Conversely, when the hot water heating load is lower than expected, if the amount of hot water stored is excessive after hot water is supplied to the hot water supply load and the hot water storage time until the next heat demand is used becomes longer, heat dissipation will reduce energy efficiency. Invite.

  Therefore, when the hot water supply to the hot water heating apparatus is covered by the thermal energy generated by the cogeneration apparatus, if the hot water usage state of the hot water heating apparatus can be predicted with high accuracy, the cogeneration apparatus can be operated with high efficiency.

  The present invention has been made in view of the above-described problems, and an object of the present invention is to solve the above-described problems and to predict the hot water usage state of the hot water heating apparatus with high accuracy in consideration of changes in weather conditions. It is to provide a heating load prediction system.

  In order to achieve this object, a first characteristic configuration of a hot water heating load prediction system according to the present invention includes an operation information input unit that receives input of operation information related to a hot water usage state of a hot water heating device, and a predetermined one or more types A meteorological observation data input unit for receiving the input of the meteorological observation data, and an operation information database creating unit for storing the driving information and the meteorological observation data in the driving information database in association with each other each time the input is received, A weather forecast data input unit for receiving a forecast value of the predetermined weather observation data; and the correspondence between the driving information and the weather observation data stored in the driving information database based on the forecast value. And a thermal load predicting unit that predicts the hot water usage state.

According to the first feature configuration, since the operation information of the hot water heater is stored in the operation information database in association with, for example, weather data such as temperature and precipitation (apart from clear rain), the weather forecast data Based on the forecast value of the weather observation data input to the input unit, accurate prediction reflecting the weather condition of the operation information of the hot water heater can be performed from the corresponding relationship stored in the operation information database. As a result, when the prediction result is used for the operation control of the cogeneration apparatus, the cogeneration apparatus can be operated with high energy efficiency, the operation cost of the cogeneration apparatus is reduced, the energy is saved, and the CO ₂ emission is reduced. Etc. are promoted comprehensively.

  In the second feature configuration of the hot water heating load prediction system, in addition to the first feature configuration, the operation information database creation unit is configured to correspond the operation information stored in the operation information database to the weather observation data. Based on the relationship, a correspondence between the predetermined statistical value of the operation information and the meteorological observation data is generated and stored in the operation information database, and the thermal load prediction unit is configured to generate the operation information based on the prediction value. The hot water use state of the hot water heater is predicted from the correspondence between the predetermined statistical value of the operation information stored in the database and the weather observation data.

  According to the second feature configuration, the correspondence between the driving information stored in the driving information database and the weather observation data is processed by statistical processing as the correspondence between the predetermined statistical value of the driving information and the weather observation data. Therefore, the prediction of the thermal load prediction unit can be executed more accurately and easily.

  In the third feature configuration of the hot water heating load prediction system, in addition to the second feature configuration, the operation information database creation unit categorizes the weather observation data into a plurality of data categories, and stores the data in the operation information database. Based on the correspondence between the accumulated driving information and the weather observation data, a cumulative value of the driving information for each data section is derived, and the correspondence between the predetermined statistical value of the driving information and the weather observation data Is to generate.

  According to the third feature configuration, since the heat load prediction unit obtains the statistical value of the operation information of the hot water heating appliance by determining which data category the forecast value of the weather observation data belongs to, the statistical value Based on this, it is possible to easily and accurately predict the hot water usage state of the hot water heater.

  The fourth characteristic configuration of the cogeneration apparatus includes, in addition to the third characteristic configuration described above, when the predetermined statistical value of the operation information indicates that the operation information is an on / off state of the hot water heater, When the operation information is the amount of heat consumed or the amount of hot water used per unit time of the hot water heater, the distribution of the amount of heat consumed or the amount of hot water used for each data category Or it is the point which is a statistical value which prescribes | regulates the distribution shape.

  According to the third or fourth feature configuration, the thermal load prediction unit can obtain the statistical value of the operation information of the hot water heating appliance by determining which data category the forecast value of the weather observation data belongs to. Based on the statistical value, it is possible to easily and accurately predict the hot water usage state of the hot water heating device, for example, the on / off state of the hot water heating device, the heat consumption per unit time or the heat load of the hot water usage amount.

  In the fifth feature configuration of the hot water heating load prediction system, in addition to the fourth feature configuration, in the case where the operation information is an on / off state of the hot water heater, the thermal load prediction unit is configured to turn on or The data segment in which the off probability changes markedly is a singular point, and the on / off state of the hot water heater is predicted based on the comparison result between the forecast value and the singular point.

  According to the fifth characteristic configuration described above, the on / off determination of the operation state of the hot water heater can be made very easily and accurately by determining which data category the forecast value of the weather observation data belongs to.

  In the sixth feature configuration of the hot water heating load prediction system, in addition to any one of the feature configurations described above, the operation information input unit accepts input of the operation information every predetermined time, and the weather observation data input unit , Receiving the input of the weather observation data every predetermined time, the operation information database creation unit stores the operation information and the weather observation data every predetermined time in the operation information database, and the thermal load prediction unit The hot water use state of the hot water heater is predicted every predetermined time.

  According to the sixth characteristic configuration, it is possible to accurately predict the weather condition of the operation information for each time zone of the hot water heater. As a result, the prediction result for each time zone can be used for operation control of the cogeneration apparatus, and the cogeneration apparatus can be operated with high energy efficiency.

  An embodiment of a hot water heating load prediction system according to the present invention (hereinafter referred to as “the present invention system” as appropriate) will be described with reference to the drawings.

<First Embodiment>
As shown in FIG. 1, the system 10 of the present invention is used to control a household gas cogeneration apparatus 20 that uses city gas as a fuel source and simultaneously generates electric power and heat energy.

  The gas cogeneration apparatus 20 includes a generator unit 23 including a gas engine 21 that outputs mechanical rotational energy by combustion of city gas, and a generator 22 that converts the rotational energy into electrical energy and outputs alternating current power. System interconnection for converting the first AC power generated by 22 into the second AC power having the same voltage and frequency as the commercial power system, and for connecting the converted second AC power with the commercial power system The heat energy recovered by the inverter 24, the heat exchanger 25 for recovering the exhaust heat generated from the gas engine 21, and the heat exchanger 25 is converted into hot water heating equipment 31 such as floor heating or bathroom dryer, An exhaust heat utilization hot water supply and heating unit 26 for use in a heat load 30 including a hot water supply device 32 used for a hot water bath of a bath and a generator unit 23 Configured with the operation control system 27 for controlling the operation.

  At the time of power generation by the gas cogeneration apparatus 20, the power generated by the gas cogeneration apparatus 20 (hereinafter simply referred to as “generated power”) is commercial power supplied from a commercial power system (for example, single-phase three-wire AC 100V / 200V ( 50 Hz or 60 Hz)) and the power is supplied to the power load 40 in the home. Here, the power load 40 includes power demand generated in household appliances such as refrigerators and televisions used in ordinary households, lighting fixtures, and the like.

  The waste heat utilization hot water supply / heating unit 26 is provided with a hot water storage tank (not shown) for temporarily storing the thermal energy recovered by the heat exchanger 25 in a hot water state. Hot water can be supplied from the hot water storage tank according to the occurrence of (example). In addition, the exhaust heat-utilizing hot water supply / heating unit 26 is provided with heat exchangers (not shown) for the hot water heating devices 31 and the hot water supply devices 32, and the heat exchanger 25 for each heat load. The heat energy recovered in (1) can be used. Therefore, the heat load 30 of the exhaust heat utilization hot water supply / heating unit 26 includes hot water supply demand generated by various hot water heating devices 31 and hot water supply devices 32.

  Here, the hot water supply flow rate and the hot water supply temperature from the waste heat utilization hot water supply and heating unit 26 to the hot water heating device 31 are measured by the measuring device 33 (flow meter and thermometer). In addition, the hot water supply flow rate and the hot water supply temperature measured by the measuring instrument 33 are transmitted to the present invention system 10 at regular time intervals (for example, 10 minutes to 1 hour) and converted into heat quantity. In addition, you may perform conversion to a calorie | heat amount by the measuring instrument 33 side. Further, the measuring instrument 34 is configured to be able to determine the on / off state of the hot water heating device 31 for every predetermined time width and transmit it to the system 10 of the present invention as a part of the operation information related to the hot water heating device 31.

  As shown in FIG. 2, the system 10 of the present invention includes an operation information input unit 1, a weather observation data input unit 2, an operation information database creation unit 3, a weather forecast data input unit 4, a heat load prediction unit 5, and an operation information database 6. And a storage device 7 for storing the processing results of the respective means 1 to 5 and the operation information database 6. Each means 1 to 5 of the present invention system 10, the operation information database 6, and the storage device 7 are configured using a computer system such as a microcomputer, and are stored in a built-in memory, which will be described in detail later. The hot water usage state of the hot water heater 31 is predicted with high accuracy in consideration of changes in weather conditions according to the processing contents of each program of the operation information collection process and the hot water heating load prediction process.

  The system 10 of the present invention is configured so that the operation information input unit 1 can accept input of operation information related to the hot water usage state of the hot water heating equipment 31 measured by the measuring instrument 33, and the weather observation data input unit 2 Meteorological data is input from the meteorological data providing apparatus 50 via the network 51 such as the Internet, and the weather forecast data input unit 4 predicts the weather forecast data from the meteorological data providing apparatus 50 via the network 51 such as the Internet or a communication line. It is configured to accept values.

  The meteorological observation data transmitted from the meteorological data providing device 50 includes information such as the temperature, precipitation, sunshine duration, and wind direction of the area where the cogeneration device 20 is installed. In this embodiment, meteorological data For example, the providing device 50 collectively from an external weather data providing organization, for example, in the past week, or in the case of forecast values of meteorological observation data, for a certain period of time such as 1 to 2 hours in a certain period such as the next week from now Meteorological observation data is stored in a storage device (not shown) connectable to the meteorological data providing device 50 and stored in the meteorological observation data input means 2 or the weather forecast data input unit 4 of the system 10 of the present invention, for example, 1 Input of meteorological observation data distributed at predetermined intervals such as once a day and distributed by meteorological observation data input means 2 or weather forecast data input unit 4 It employs a configuration for storing a value or forecast value in the storage device 7.

  Next, the functions of the respective means 1 to 5 of the system 10 of the present invention and the processing operations of the operation information collection process and hot water heating load prediction process processed by them will be described with reference to the illustrated flowchart. In the present embodiment, the hot water heating device 31 is exemplified by floor heating that is greatly affected by the temperature in use, and the operation information input unit 1 transmits the operation information of the hot water heating device 31 from the measuring instrument 33. The hot water heater 31 is turned on and off every hour, the weather observation data input unit 2 receives hourly weather observation data from the weather data providing device 50, and the weather forecast data input unit 4 provides weather data providing device. The forecast value of meteorological observation data every 50 to 3 hours is accepted.

  First, the driving information collection process will be described with reference to the flowchart shown in FIG. Here, for convenience of explanation, the processing start timing of the driving information collecting process is to execute the driving information collecting process of the previous day every day together with the date change. In the following, the driving information collecting process is executed at 0:00 on January 20 An example is shown.

  At 0 o'clock on January 20, the meteorological observation data input unit 2 receives hourly weather observation data from 0 o'clock to 24:00 on January 19 via the network 51 from the meteorological data providing device 50 (# a1 ), Necessary information is extracted from the received weather observation data and stored in the storage device 7 (# a2). Further, the operation information input unit 1 receives from the measuring device 33 the on / off state for each hour from 0 o'clock to 24:00 on January 19 of the hot water heater 31 as operation information (# a3). The driving information database creation unit 3 associates the meteorological observation data and driving information received by the system 10 of the present invention with each other according to time zones classified in increments of one hour from 0 o'clock as shown in FIG. 4 (a), for example. And stored in the driving information database 6 (# a4). Here, in FIG. 4A, when the hot water heater 31 is in the on state, the on state flag is set to 1. When the hot water heater 31 is in the off state, the off state flag is set to 1. Adopts precipitation, daylight hours. In FIG. 4 (a), for example, the temperature in the time zone from 10:00 to 59 minutes on January 19 is 8.7 ° C., precipitation is not measured, the sunshine duration is 0.6 hours, and hot water heating. It shows that the device 31 is in the on state.

  After the driving information database 6 is created, the driving information database creation unit 3 continues to generate the meteorological observation data in the storage device 7 in order to generate a correspondence between predetermined statistical values of the driving information and the meteorological observation data. (# A5). For example, it reads from the storage device 7 data categories that classify the air temperature into a plurality of categories such as −4 ° C. to −2 ° C., −2 ° C. to less than 0 ° C., 0 ° C. to less than 2 ° C. For the belt, the data category corresponding to the meteorological observation data (here, temperature) is extracted.

  Then, a counter is provided for each data segment in each time zone, and when the hot water heater 31 is in an on state, for example, 1 is added and counted up. Here, when the driving information collection process has already been executed before January 19, the driving information database 6 stores the cumulative value of the counter for each data section in each time zone. The creation unit 3 first reads the cumulative value of the on-state counter stored in the operation information database 6 (# a6), and if the hot water heating appliance 31 is on for each time zone, 1 is added to the counter, the accumulated value of the updated counter is stored in the driving information database 6 (# a7), and the correspondence between the statistical value of the driving information and the weather observation data is stored in the driving information database 6 (# a8). ). When the meteorological observation data on January 19 has the contents illustrated in FIG. 4A, the statistics of the on-state of the hot water heater 31 generated by the processing steps # a5 to # a7 and the meteorological observation data The correspondence relationship is illustrated in FIG. According to FIG. 4 (a), for example, the temperature in the time zone from 10:00 to 59 minutes is 8.7 ° C., and the hot water heater 31 is in the on state. 1 is counted in the item that intersects the row of the data section that is less than the column of the 10 o'clock time zone. In the above description, the case where 1 is added to the counter when the hot water heater 31 is in the on state has been described as an example. However, in the processing steps # a5 to # a7, the case of the off state is similarly processed, Assume that the correspondence between the statistical value of the device 31 in the off state and the weather observation data is generated.

  Next, the hot water heating load prediction process will be described with reference to the flowchart shown in FIG. Here, for convenience of explanation, it is assumed that the processing start timing of the hot water heating load prediction process is executed every day at 23:00 on the previous day, and the following shows an example when the hot water heating load prediction process is executed at 23:00 on January 20th. .

  After 23:00 on January 20th, the weather forecast data input unit 4 receives forecast values of weather observation data every 3 hours from 00:00 to 24:00 on January 20 via the network 51 from the weather data providing device 50 Then, necessary information is extracted from the forecast value of the received weather observation data (# b1) and stored in the storage device 7 (# b2). Next, the thermal load prediction unit 5 reads the correspondence relationship between the statistical value of the operation information (on state and off state) already generated by the operation information database creation unit 3 and the weather observation data from the operation information database 6 (# b3) Based on the forecast value of the weather observation data, the cumulative value of the on-time and off-state counters of the corresponding time zone and data section is read as the statistical value of the driving information (# b4). For example, if the correspondence between the statistical value of the driving information generated by the driving information database creation unit 3 and the weather observation data is the content illustrated in FIG. 6A, the forecast value of the weather observation data is as shown in FIG. As shown in (a) of (b), for example, when the forecast value of weather observation data at 3 o'clock is 4 ° C, the corresponding time zone read at 3 o'clock based on the forecast value of the weather observation data The accumulated values of the on-state and off-state counters of 4 ° C. or more and less than 6 are 0 and 2, respectively.

  In FIG. 6 (a), the cumulative value is described based on data for three days. However, in actuality, the cumulative value also increases as the number of operating days of the gas cogeneration device 20 increases. After the operation period elapses, the cumulative value in FIG. 6A may be on the order of tens or hundreds.

  Further, the thermal load predicting unit 5 compares, for example, the accumulated values of the counters in the on state and the off state from the statistical value of the operation information obtained in the processing step (# b4), and determines the state with the larger accumulated value as hot water heating. It is determined as a prediction of the hot water usage state of the device 31 (# b5). That is, taking the time zone of 3 o'clock in FIG. 6B as an example, the accumulated value of the counter is larger in the off state than in the on state. In other words, the on probability (probability of being in the on state) is the off probability. Since it is higher, the prediction of the hot water usage state is in an off state (see FIGS. 6B and 6C). After the forecast of the forecast value of the weather observation data is determined, the forecast of the forecast value of the meteorological observation data is predicted for one hour before and after the forecast time zone of the meteorological observation data. The hot water usage state of the hot water heater 31 is predicted by a method of developing the prediction of the belt (# b6).

  In the present embodiment, the accumulated values of the on-state and off-state of the hot water heater 31 that are easily generated by the operation information collection process are simply determined by comparing the accumulated values of the on-state and the off-state by time in the hot-water heating load prediction process. On-off prediction can be performed by comparing probability and off-probability.

  As described above, it is possible to predict in advance the on / off state for each time zone of floor heating based on the temperature prediction of the current day or the next day. As a result, since the prediction result can be used for control of the cogeneration apparatus 20, the cogeneration apparatus can be operated with high energy efficiency.

Second Embodiment
In the first embodiment, as the hot water heater 31, a method of classifying the air temperature in increments of 2 ° C. as an example of the data classification for classifying the weather observation data into a plurality of examples, with the floor heating that is greatly influenced by the air temperature in use. However, the present invention is not limited to this. For example, the hot water heater 31 may be a bathroom dryer that is affected not only by precipitation but also by sunshine hours in use. The processing operation of the driving information collection process in this case will be described with reference to the drawings. Note that FIG. 3 is used as the processing flow diagram. Here, for convenience of explanation, the processing start timing of the driving information collecting process is to execute the driving information collecting process of the previous day every day together with the date change. In the following, the driving information collecting process is executed at 0:00 on January 20 An example is shown.

  At 0 o'clock on January 20, the meteorological observation data input unit 2 receives hourly weather observation data from 0 o'clock to 24:00 on January 19 via the network 51 from the meteorological data providing device 50 (# c1 ), Necessary information is extracted from the received weather observation data and stored in the storage device 7 (# c2). Further, the operation information input unit 1 accepts, as operation information, the hourly on / off state of the hot water heater 31 from the measuring device 33 from midnight to 24:00 on January 19 (# c3). The driving information database creation unit 3 correlates the meteorological observation data and driving information received by the system 10 of the present invention with each other according to time zones classified in increments of one hour from 0 o'clock as shown in FIG. 7A, for example. And stored in the driving information database 6 (# c4). Here, in FIG. 7A, when the hot water heater 31 is in the on state, the on state flag is set to 1. When the hot water heater 31 is in the off state, the off state flag is set to 1. Adopts precipitation, daylight hours. In FIG. 7 (a), for example, the air temperature in the time zone from 10:00 to 59 minutes on January 19 is 8.7 ° C., precipitation is not measured, and the sunshine duration is 0.6 hours. It shows that the device 31 was in the off state.

  After the driving information database 6 is created, the driving information database creation unit 3 continues to generate the meteorological observation data in the storage device 7 in order to generate a correspondence between predetermined statistical values of the driving information and the meteorological observation data. (# C5). For example, the sunshine time is 0.2 hours or more and there is no precipitation, the sunshine time is less than 0.2 hours and there is no precipitation, the sunshine time is 0.2 hours or more and there is precipitation, the sunshine time A data category that is classified into a plurality of categories, such as less than 0.2 hours and precipitation, is read from the storage device 7, and for each time zone, the weather observation data (here, sunshine duration and precipitation) corresponds to the data category. To extract.

  Then, a counter is provided for each data segment in each time zone, and when the hot water heater 31 is in an on state, for example, 1 is added and counted up. Here, when the driving information collection process has already been executed before January 19, the driving information database 6 stores the cumulative value of the counter for each data section in each time zone. The creation unit 3 first reads the cumulative value of the on-state counter stored in the operation information database 6 (# c6), and if the hot water heating appliance 31 is on for each time zone, 1 is added to the counter, the accumulated value of the updated counter is stored in the driving information database 6 (# c7), and the correspondence between the statistical value of the driving information and the weather observation data is stored in the driving information database 6 (# c8). ). When the meteorological observation data on January 19 has the contents illustrated in FIG. 4A, the statistics of the on-state of the hot water heating equipment 31 generated by the processing steps # c5 to # c7 and the meteorological observation data The correspondence relationship is illustrated in FIG. According to FIG. 7 (a), for example, the sunshine duration in the time zone from 20 hours 0 minutes to 59 minutes is 0 hours, there is precipitation, and the hot water heater 31 is on. 1 is counted in the item where the row of the data division in which the time is less than 0.2 hours and there is precipitation and the column of the time zone at 20:00. In the above description, the case where 1 is added to the counter when the hot water heater 31 is in the on state has been described as an example. However, in the processing steps # c5 to # c7, the case of the off state is similarly processed, Assume that the correspondence between the statistical value of the device 31 in the off state and the weather observation data is generated.

  Since the hot water heating load prediction process is the same as that of the first embodiment, the description thereof is omitted.

  Hereinafter, another embodiment will be described.

  <1> In each of the embodiments described above, the operation information is set to the on / off state of the hot water heater 31. However, the present invention is not limited to this. For example, the hot water supply flow rate and the hot water temperature of the hot water heater 31 measured by the measuring device 33 are integrated. The amount of heat per unit time sent to the hot water heating device 31 is obtained by the above, and is used as the amount of heat consumed per unit time of the hot water heating device 31, and the consumption heat amount or the amount of hot water used is the operation information as the operation information of the hot water heating device 31. It is also preferable to store in the database 6.

  In this case, in order to obtain the predetermined statistical value of the driving information, for example, the average value and the standard deviation of the driving information are obtained in a predetermined period for each data category and time zone, and the daily driving information collecting process is performed. The driving information database 6 is updated. Then, during the hot water heating load prediction process, based on the forecast value of the weather observation data by time zone, the statistical value (average value, standard deviation) of the operation information of the data category corresponding to each forecast value by time zone is operated information Extracted from the database 6, the statistics for one day are output to the operation control system 27 of the gas cogeneration apparatus 20. Thereby, the operation control system 27 can obtain an average hot water heating load prediction for each time zone of the day, an active hot water heating load prediction considering a standard deviation, and a passive hot water heating load prediction. it can.

  <2> In the first embodiment, the cumulative value of the on-state and the off-state for each time of the hot water heating appliance 31 is generated in the operation information collection process, and the data classification corresponding to the forecast value in the hot water heating load prediction process However, instead of this, in the driving information collection process, the driving information database 6 is determined with the data section where the cumulative value or probability of the ON state and the OFF state greatly changes as a singular point. May be recorded for each time zone, and in the hot water heating load prediction process, the predicted value and the singular point may be compared to perform on / off prediction. It should be noted that a singular point whose accumulated value changes greatly coincides with a singular point whose probability greatly changes.

  For example, for the singular point in the on state, when the weather observation data is air temperature, when the data segment is moved to the one-step high temperature side, the data segment whose accumulated value decreases significantly is extracted and stored as a singular point. . On the other hand, for the singular point in the off state, when the data section is moved to the low temperature side by one stage, the data section whose accumulated value is significantly reduced is extracted and stored as a singular point. Then, during the hot water heating load prediction process, the forecast value is compared with each singular point of the on state and the off state for each time zone, and if the forecast value is lower than the singular point of the on state, the on state is predicted, If it is on the side, the off state is predicted. If the predicted value is lower than the singular point in the off state, the on state is predicted, and if the predicted value is on the high temperature side, the off state is predicted. Here, when the singular point in the on state and the singular point in the off state are separated, the prediction by the singular point in the on state and the prediction by the singular point in the off state are performed when the data segment between them matches the forecast value. In this case, it is preferable to correct the singularity based on the magnitude relationship between the accumulated values of the on state and the off state in the data section.

  By extracting singular points in advance and performing on / off prediction based on the singular points, the forecast value is abnormally high or low in the same time zone at the same time, and the data classification of the time zone is Even if there is no cumulative value of either ON or OFF, it is possible to make a prediction, which is convenient.

  <3> In the operation information collection process of each of the above embodiments, the case where the operation information input unit 1 receives operation information for 24 hours of the hot water heating device 31 from the measuring instrument 33 in step # a3 or # c3 is exemplified. The driving information may be received sequentially, for example, every hour before starting the driving information collection process, and stored in a predetermined storage area of the storage device 7.

  <4> In each of the embodiments described above, the gas cogeneration apparatus 20 has been described by taking the gas engine cogeneration apparatus in which the generator unit 23 includes the gas engine 21 and the generator 22 as an example, but the generator unit 23 generates city gas. A fuel cell as a fuel source may be used. Further, the gas cogeneration apparatus 20 may be a cogeneration apparatus other than the gas engine / cogeneration apparatus and the fuel cell cogeneration apparatus. Furthermore, the gas cogeneration apparatus 20 is not limited to a cogeneration apparatus that uses city gas as a fuel source.

The block block diagram which shows the example of 1 structure of the gas cogeneration apparatus which is the control object of the hot water heating load prediction system which concerns on this invention The block block diagram which shows one Embodiment of the hot water heating load prediction system which concerns on this invention The flowchart which shows the operation | movement procedure of the driving | operation information collection process in one Embodiment of the hot water heating load prediction system which concerns on this invention. Example diagram showing correspondence between predetermined statistical values of driving information and meteorological observation data The flowchart which shows the operation | movement procedure of the hot water heating load prediction process in one Embodiment of the hot water heating load prediction system which concerns on this invention. Example diagram showing correspondence between predetermined statistical values of driving information and meteorological observation data Example diagram showing correspondence between predetermined statistical values of driving information and meteorological observation data

Explanation of symbols

1: Operation information input unit 2: Weather observation data input unit 3: Operation information database creation unit 4: Weather forecast data input unit 5: Thermal load prediction unit 6: Operation information database 7: Storage device 10: Invention system 20: Gas cogeneration 21: Gas engine 22: Generator 23: Generator unit 24: Inverter 25: Heat exchanger 26: Waste heat utilization hot water heating / heating unit 27: Operation control system 30: Thermal load 31: Hot water heating equipment 32: Hot water heating equipment 33 : Measuring instrument 40: Electric power load 50: Weather data providing device 51: Network

Claims (6)

A hot water heating load prediction system for predicting the hot water usage state of a hot water heater,
An operation information input unit that receives input of operation information related to the hot water use state of the hot water heating device;
A meteorological observation data input unit for receiving input of predetermined one or more types of meteorological observation data;
A driving information database creation unit that stores the driving information and the meteorological observation data in a driving information database in association with each other each time an input is received,
A weather forecast data input unit for receiving a forecast value of the predetermined weather observation data;
A thermal load prediction unit that predicts the hot water usage state of the hot water heating appliance from the correspondence relationship between the operation information stored in the operation information database and the weather observation data based on the forecast value;
A hot water heating load prediction system comprising: The driving information database creation unit generates a correspondence between the predetermined statistical value of the driving information and the weather observation data based on the correspondence between the driving information and the weather observation data accumulated in the driving information database. Stored in the driving information database,
The thermal load prediction unit predicts the hot water usage state of the hot water heating appliance from the correspondence between the predetermined statistical value of the operation information stored in the operation information database and the weather observation data based on the predicted value The hot water heating load prediction system according to claim 1.   The driving information database creation unit classifies the weather observation data into a plurality of data sections, and based on the correspondence between the driving information stored in the driving information database and the weather observation data, the data for each data section The hot water heating load prediction system according to claim 2, wherein a cumulative value of the operation information is derived to generate a correspondence relationship between the predetermined statistical value of the operation information and the weather observation data.   When the predetermined statistical value of the operation information is the ON / OFF state of the hot water heater, the ON / OFF probability in each of the data sections, and the operation information is a unit of the hot water heater. 4. The statistical value that defines the distribution of the heat consumption or the amount of hot water used for each of the data sections or the shape of the distribution when the amount of heat consumed or the amount of hot water used per hour is specified. 5. Hot water heating load prediction system. In the case where the operation information is an on / off state of the hot water heater,
The thermal load predicting unit predicts an on / off state of the hot water heating appliance based on a comparison result between the forecast value and the singular point, with the data classification in which the probability of on or off changes significantly as a singular point. The hot water heating load prediction system according to claim 4. The driving information input unit accepts input of the driving information every predetermined time,
The meteorological observation data input unit accepts the input of the meteorological observation data every predetermined time;
The driving information database creation unit stores the driving information and the weather observation data for each predetermined time in a driving information database,
The hot water heating load prediction system according to any one of claims 1 to 5, wherein the thermal load prediction unit predicts a hot water usage state of the hot water heating appliance every predetermined time.

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