Heating system load prediction method, system and medium based on big data

Abstract

The present application provides a method, system and medium for load prediction of a heating system based on big data. The method includes: obtaining load operation data and corresponding influencing factor data of a preset heating system within a preset time period, segmenting the load operation data and corresponding influencing factor data according to a time series, obtaining load operation data and influencing factor data corresponding to n time subsequences, and weighted processing to obtain a load fluctuation index, extracting statistical characteristic data of the load operation data, processing to obtain a load shape index, obtaining energy data within a preset time period, and processing to obtain a load efficiency characteristic index, combining the load fluctuation index and the load shape index for processing, obtaining a load comprehensive evaluation index, comparing the load comprehensive evaluation index with a preset load comprehensive evaluation threshold, and performing heating system load prediction according to the comparison result, thereby realizing a technology for heating system load prediction based on big data.

Description

Technical Field

[0001] The present application relates to the technical field of load forecasting, and in particular, to a method, system and medium for heat supply system load forecasting based on big data. Background Art

[0002] In heating systems, accurate prediction of heat load is crucial to achieve efficient heating, save energy and improve user comfort.

[0003] Traditional load forecasting methods for heating systems are often based on simple statistical models or empirical formulas, with limited accuracy and adaptability. Even those based on big data are relatively simple and cannot comprehensively evaluate the heating system.

[0004] In view of the above problems, effective technical solutions are urgently needed. Summary of the invention

[0005] The purpose of the present application is to provide a method, system and medium for load prediction of a heating system based on big data. By performing multi-feature processing on the load operation data of the heating system and the corresponding influencing factor data, a comprehensive load evaluation index can be obtained, and the characteristics of the heating system can be judged accordingly. Based on this, the load of the heating system can be predicted, thereby realizing a technology for load prediction of a heating system based on big data.

[0006] The present application also provides a heating system load prediction method based on big data, comprising the following steps:

[0007] Obtaining load operation data of a preset heating system within a preset time period and corresponding influencing factor data;

[0008] The load operation data and the corresponding influencing factor data are divided according to the time series to obtain the load operation data and the influencing factor data corresponding to n time subsequences, and the load fluctuation index is obtained by weighted processing;

[0009] Extracting statistical characteristic data of the load operation data, and processing to obtain a load shape index;

[0010] Acquiring energy data within the preset time period, and processing to obtain a load efficiency characteristic index;

[0011] According to the load fluctuation index and the load shape index, combined with the load efficiency characteristic index, a comprehensive load evaluation index is obtained;

[0012] The load comprehensive evaluation index is compared with a preset load comprehensive evaluation threshold, and the heating system load is predicted based on the comparison result.

[0013] Optionally, in the method for load prediction of a heating system based on big data described in the present application, the step of obtaining load operation data of a preset heating system within a preset time period and corresponding influencing factor data includes:

[0014] Obtaining load operation data of a preset heating system within a preset time period and corresponding influencing factor data;

[0015] The load operation data includes water supply temperature data, water supply pressure data and water supply flow data;

[0016] The influencing factor data include indoor set temperature data, outdoor temperature data and solar radiation data.

[0017] Optionally, in the method for load forecasting of a heating system based on big data described in the present application, the load operation data and the corresponding influencing factor data are segmented according to the time series to obtain the load operation data and the influencing factor data corresponding to n time subsequences, and the load fluctuation index is obtained by weighted processing, including:

[0018] Processing the preset time period according to the time series to obtain n time subsequences;

[0019] Segment the load operation data and the corresponding influencing factor data according to the n time subsequences to obtain the load operation data and the influencing factor data corresponding to the n time subsequences;

[0020] Obtaining total heat load data within the preset time period;

[0021] The load fluctuation index is obtained by performing weighted processing based on the load operation data and the influencing factor data corresponding to the n time subsequences in combination with the total heat load data.

[0022] Optionally, in the method for load prediction of a heating system based on big data described in the present application, extracting statistical characteristic data of the load operation data and processing to obtain a load shape index includes:

[0023] Extracting statistical characteristic data corresponding to the water supply temperature data, water supply pressure data and water supply flow data;

[0024] The statistical characteristic data includes mean data and standard deviation data;

[0025] The load shape index is obtained by processing the mean data and the standard deviation data corresponding to the water supply temperature data, the water supply pressure data and the water supply flow rate data.

[0026] Optionally, in the method for load prediction of a heating system based on big data described in the present application, the step of acquiring the energy data within the preset time period and processing to obtain a load efficiency characteristic index includes:

[0027] Acquiring energy data within the preset time period, including fuel energy data and heating data;

[0028] The fuel energy data and the heat supply data are processed to obtain a load efficiency characteristic index.

[0029] Optionally, in the method for load prediction of a heating system based on big data described in the present application, the load fluctuation index and the load shape index are processed in combination with the load efficiency characteristic index to obtain a load comprehensive evaluation index, including:

[0030] According to the load fluctuation index and the load shape index, combined with the load efficiency characteristic index, a preset load multi-feature processing model is used for processing to obtain a load comprehensive evaluation index.

[0031] In a second aspect, the present application provides a heating system load prediction system based on big data, the system comprising: a memory and a processor, the memory comprising a program of a heating system load prediction method based on big data, the program of the heating system load prediction method based on big data being executed by the processor to implement the following steps:

[0032] Obtaining load operation data of a preset heating system within a preset time period and corresponding influencing factor data;

[0033] The load operation data and the corresponding influencing factor data are divided according to the time series to obtain the load operation data and the influencing factor data corresponding to n time subsequences, and the load fluctuation index is obtained by weighted processing;

[0034] Extracting statistical characteristic data of the load operation data, and processing to obtain a load shape index;

[0035] Acquiring energy data within the preset time period, and processing to obtain a load efficiency characteristic index;

[0036] According to the load fluctuation index and the load shape index, combined with the load efficiency characteristic index, a comprehensive load evaluation index is obtained;

[0037] The load comprehensive evaluation index is compared with a preset load comprehensive evaluation threshold, and the heating system load is predicted based on the comparison result.

[0038] Optionally, in the heating system load prediction system based on big data described in the present application, the step of obtaining the load operation data of the preset heating system within a preset time period and the corresponding influencing factor data includes:

[0039] Obtaining load operation data of a preset heating system within a preset time period and corresponding influencing factor data;

[0040] The load operation data includes water supply temperature data, water supply pressure data and water supply flow data;

[0041] The influencing factor data include indoor set temperature data, outdoor temperature data and solar radiation data.

[0042] Optionally, in the heating system load forecasting system based on big data described in the present application, the load operation data and the corresponding influencing factor data are segmented according to the time series to obtain the load operation data and the influencing factor data corresponding to n time subsequences, and the load fluctuation index is obtained by weighted processing, including:

[0043] Processing the preset time period according to the time series to obtain n time subsequences;

[0044] Segment the load operation data and the corresponding influencing factor data according to the n time subsequences to obtain the load operation data and the influencing factor data corresponding to the n time subsequences;

[0045] Obtaining total heat load data within the preset time period;

[0046] The load fluctuation index is obtained by performing weighted processing based on the load operation data and the influencing factor data corresponding to the n time subsequences in combination with the total heat load data.

[0047] In the third aspect, the present application also provides a computer-readable storage medium, which stores a heating system load prediction method program based on big data. When the heating system load prediction method program based on big data is executed by a processor, the steps of the heating system load prediction method based on big data as described in any one of the above items are implemented.

[0048] As can be seen from the above, the method, system and medium for load prediction of a heating system based on big data provided by the present application obtain the load operation data and the corresponding influencing factor data of a preset heating system within a preset time period, divide the load operation data and the corresponding influencing factor data according to the time series, obtain the load operation data and the influencing factor data corresponding to n time subsequences, and perform weighted processing to obtain the load fluctuation index, extract the statistical characteristic data of the load operation data, process to obtain the load shape index, obtain the energy data within a preset time period, and process to obtain the load efficiency characteristic index, according to the load fluctuation index and the load shape index, combined with the load efficiency characteristic index, process to obtain the load comprehensive evaluation index, compare the load comprehensive evaluation index with the preset load comprehensive evaluation threshold, and perform heating system load prediction according to the comparison result, thereby realizing the technology of heating system load prediction based on big data.

[0049] Other features and advantages of the present application will be described in the following description, and partly become apparent from the description, or understood by practicing the embodiments of the present application. The purpose and other advantages of the present application can be realized and obtained by the structures specifically pointed out in the written description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0050] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0051] Figure 1 A flow chart of a method for predicting heating system load based on big data provided in an embodiment of the present application;

[0052] Figure 2 A flow chart of obtaining a load fluctuation index in a method for predicting a heating system load based on big data provided in an embodiment of the present application;

[0053] Figure 3 A flow chart of obtaining a load shape index for a heating system load prediction method based on big data provided in an embodiment of the present application;

[0054] Figure 4 A flow chart of obtaining a load efficiency characteristic index for a heating system load forecasting method based on big data provided in an embodiment of the present application. DETAILED DESCRIPTION

[0055] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.

[0056] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0057] Please refer to Figure 1 , Figure 1 The following is a flow chart of a method for predicting the load of a heating system based on big data in some embodiments of the present application. The method for predicting the load of a heating system based on big data is used in terminal devices, such as computers, mobile terminals, etc. The method for predicting the load of a heating system based on big data includes the following steps:

[0058] S11, obtaining load operation data of a preset heating system within a preset time period and corresponding influencing factor data;

[0059] S12, dividing the load operation data and the corresponding influencing factor data according to the time series, obtaining the load operation data and the influencing factor data corresponding to n time subsequences, and performing weighted processing to obtain the load fluctuation index;

[0060] S13, extracting statistical characteristic data of the load operation data, and processing to obtain a load shape index;

[0061] S14, acquiring energy data within the preset time period, and processing to obtain a load efficiency characteristic index;

[0062] S15, processing the load fluctuation index and the load shape index in combination with the load efficiency characteristic index to obtain a load comprehensive evaluation index;

[0063] S16. Compare the load comprehensive evaluation index with a preset load comprehensive evaluation threshold, and perform heating system load forecasting based on the comparison result.

[0064] It should be noted that, in the heating system, accurate prediction of heat load is crucial to achieve efficient heating, save energy and improve user comfort. Traditional heating system load prediction methods are often based on simple statistical models or empirical formulas, with limited accuracy and adaptability. With the development of big data technology, it is possible to use massive data resources and advanced data analysis algorithms to predict the load of the heating system. In order to more comprehensively predict the load of the heating system and improve the accuracy and reliability of the load prediction of the heating system, firstly, by obtaining the load operation data and the corresponding influencing factor data of the preset heating system in a preset time period, the load operation data and the corresponding influencing factor data are segmented according to the time series, and the load operation data and the influencing factor data corresponding to n time subsequences are obtained, and the load fluctuation index is obtained by weighted processing, the statistical characteristic data of the load operation data is extracted, and the load shape index is obtained, the energy data in the preset time period is obtained, and the load efficiency characteristic index is obtained by processing, and the load comprehensive evaluation index is obtained according to the load fluctuation index and the load shape index, combined with the load efficiency characteristic index. The load comprehensive evaluation index is compared with the preset load comprehensive evaluation threshold, and the heating system load is predicted according to the comparison result, thereby realizing the technology of heating system load prediction based on big data.

[0065] According to an embodiment of the present invention, the step of obtaining load operation data of a preset heating system within a preset time period and corresponding influencing factor data includes:

[0066] Obtaining load operation data of a preset heating system within a preset time period and corresponding influencing factor data;

[0067] The load operation data includes water supply temperature data, water supply pressure data and water supply flow data;

[0068] The influencing factor data include indoor set temperature data, outdoor temperature data and solar radiation data.

[0069] It should be noted that in order to improve the accuracy and reliability of heating system load forecasting, it is necessary to use massive data resources and advanced data analysis algorithms to conduct heating system load forecasting. It is necessary to obtain operating data related to the heating system load, including the system's water supply temperature, water supply pressure, and water supply flow data, and collect data that affects the load, including indoor set temperature, outdoor temperature, and solar radiation data.

[0070] Please refer to Figure 2 , Figure 2The present invention is a flowchart of obtaining a load fluctuation index of a method for load prediction of a heating system based on big data in some embodiments of the present invention. According to an embodiment of the present invention, the load operation data and the corresponding influencing factor data are segmented according to the time series to obtain the load operation data and the influencing factor data corresponding to n time subsequences, and the load fluctuation index is obtained by weighted processing, including:

[0071] S21, processing the preset time period according to the time series to obtain n time subsequences;

[0072] S22, dividing the load operation data and the corresponding influencing factor data according to the n time subsequences to obtain the load operation data and the influencing factor data corresponding to the n time subsequences;

[0073] S23, obtaining the total amount of heat load data within the preset time period;

[0074] S24. Perform weighted processing based on the load operation data and influencing factor data corresponding to the n time subsequences in combination with the total heat load data to obtain a load fluctuation index.

[0075] It should be noted that the load situation of the heating system will change with time, and different time windows can be selected according to the time series, such as 7 days, 30 days or seasons. In this embodiment, the preset time period is divided into 4 time subsequences according to the season, and then the obtained load operation data and the corresponding influencing factor data are divided according to the 4 time subsequences, and further weighted processing is performed in combination with the total heat load to obtain the load fluctuation index;

[0076] The calculation formula of the load fluctuation index is:

[0077] ;

[0078] in, is the load fluctuation index, is the total heat load data, , , are the water supply temperature data, water supply pressure data, and water supply flow data corresponding to the i-th in the n time subsequences, respectively. , , are the indoor set temperature data, outdoor temperature data, and solar radiation data corresponding to the i-th in the n time subsequences, , is the preset weight factor (the weight factor is obtained by querying the database of the heating system load monitoring platform).

[0079] Please refer to Figure 3, Figure 3 The flowchart of obtaining the load shape index of the heating system load prediction method based on big data in some embodiments of the present application. According to the embodiment of the present invention, the extracting statistical characteristic data of the load operation data and processing to obtain the load shape index includes:

[0080] S31, extracting statistical characteristic data corresponding to the water supply temperature data, water supply pressure data and water supply flow data;

[0081] S32, the statistical characteristic data includes mean data and standard deviation data;

[0082] S33, processing the mean data and standard deviation data corresponding to the water supply temperature data, the water supply pressure data and the water supply flow rate data to obtain a load shape index.

[0083] It should be noted that the statistical characteristic data of the load operation data, including the mean data and standard deviation data corresponding to the water supply temperature, water supply pressure and water supply flow, are extracted, and then the above data are processed to obtain the load shape index;

[0084] The calculation formula of the load shape index is:

[0085] ;

[0086] in, is the load shape index, , , They are the mean data corresponding to the water supply temperature data, water supply pressure data and water supply flow data, , , They are the standard deviation data corresponding to the water supply temperature data, water supply pressure data and water supply flow data, , , , It is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the database of the heating system load monitoring platform).

[0087] Please refer to Figure 4 , Figure 4 The present invention is a flowchart of obtaining a load efficiency characteristic index of a heating system load prediction method based on big data in some embodiments of the present application. According to an embodiment of the present invention, the energy data within the preset time period is obtained and processed to obtain the load efficiency characteristic index, including:

[0088] S41, obtaining energy data within the preset time period, including fuel energy data and heating data;

[0089] S42: Process the fuel energy data and the heating amount data to obtain a load efficiency characteristic index.

[0090] It should be noted that in order to analyze the load of the heating system more comprehensively and accurately, further obtain the efficiency characteristics of the heating system, obtain energy data within a preset time period, including fuel energy data and heating data, and process to obtain the load efficiency characteristic index;

[0091] The calculation formula of the load efficiency characteristic index is:

[0092] ;

[0093] Where, is the load efficiency characteristic index, , They are fuel energy data and heating data respectively. , It is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the database of the heating system load monitoring platform).

[0094] According to an embodiment of the present invention, the load fluctuation index and the load shape index are processed in combination with the load efficiency characteristic index to obtain a load comprehensive evaluation index, including:

[0095] According to the load fluctuation index and the load shape index, combined with the load efficiency characteristic index, a preset load multi-feature processing model is used for processing to obtain a load comprehensive evaluation index.

[0096] It should be noted that after extracting and processing the multiple features of the heating system, the load fluctuation index, load shape index and load efficiency characteristic index are obtained, and then these major indexes are input into the preset load multi-feature processing model for calculation formula processing to obtain the load comprehensive evaluation index, so as to make a more comprehensive, accurate and reliable load forecast for the heating system;

[0097] The calculation formula of the load comprehensive evaluation index is:

[0098] ;

[0099] in, is the comprehensive evaluation index of load, , , They are load fluctuation index, load shape index, and load efficiency characteristic index. , , , , It is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the database of the heating system load monitoring platform).

[0100] In a second aspect, the present invention further discloses a heating system load prediction system based on big data, comprising a memory and a processor, wherein the memory comprises a heating system load prediction method program based on big data, and when the heating system load prediction method program based on big data is executed by the processor, the following steps are implemented:

[0101] Obtaining load operation data of a preset heating system within a preset time period and corresponding influencing factor data;

[0102] The load operation data and the corresponding influencing factor data are divided according to the time series to obtain the load operation data and the influencing factor data corresponding to n time subsequences, and the load fluctuation index is obtained by weighted processing;

[0103] Extracting statistical characteristic data of the load operation data, and processing to obtain a load shape index;

[0104] Acquiring energy data within the preset time period, and processing to obtain a load efficiency characteristic index;

[0105] According to the load fluctuation index and the load shape index, combined with the load efficiency characteristic index, a comprehensive load evaluation index is obtained;

[0106] The load comprehensive evaluation index is compared with a preset load comprehensive evaluation threshold, and the heating system load is predicted based on the comparison result.

[0107] It should be noted that, in the heating system, accurate prediction of heat load is crucial to achieve efficient heating, save energy and improve user comfort. Traditional heating system load prediction methods are often based on simple statistical models or empirical formulas, with limited accuracy and adaptability. With the development of big data technology, it is possible to use massive data resources and advanced data analysis algorithms to predict the load of the heating system. In order to more comprehensively predict the load of the heating system and improve the accuracy and reliability of the load prediction of the heating system, firstly, by obtaining the load operation data and the corresponding influencing factor data of the preset heating system in a preset time period, the load operation data and the corresponding influencing factor data are segmented according to the time series, and the load operation data and the influencing factor data corresponding to n time subsequences are obtained, and the load fluctuation index is obtained by weighted processing, the statistical characteristic data of the load operation data is extracted, and the load shape index is obtained, the energy data in the preset time period is obtained, and the load efficiency characteristic index is obtained by processing, and the load comprehensive evaluation index is obtained according to the load fluctuation index and the load shape index, combined with the load efficiency characteristic index. The load comprehensive evaluation index is compared with the preset load comprehensive evaluation threshold, and the heating system load is predicted according to the comparison result, thereby realizing the technology of heating system load prediction based on big data.

[0108] According to an embodiment of the present invention, the step of obtaining load operation data of a preset heating system within a preset time period and corresponding influencing factor data includes:

[0109] Obtaining load operation data of a preset heating system within a preset time period and corresponding influencing factor data;

[0110] The load operation data includes water supply temperature data, water supply pressure data and water supply flow data;

[0111] The influencing factor data include indoor set temperature data, outdoor temperature data and solar radiation data.

[0112] It should be noted that in order to improve the accuracy and reliability of heating system load forecasting, it is necessary to use massive data resources and advanced data analysis algorithms to conduct heating system load forecasting. It is necessary to obtain operating data related to the heating system load, including the system's water supply temperature, water supply pressure, and water supply flow data, and collect data that affects the load, including indoor set temperature, outdoor temperature, and solar radiation data.

[0113] According to an embodiment of the present invention, the load operation data and the corresponding influencing factor data are segmented according to the time series to obtain the load operation data and the influencing factor data corresponding to n time subsequences, and the load fluctuation index is obtained by weighted processing, including:

[0114] Processing the preset time period according to the time series to obtain n time subsequences;

[0115] Segment the load operation data and the corresponding influencing factor data according to the n time subsequences to obtain the load operation data and the influencing factor data corresponding to the n time subsequences;

[0116] Obtaining total heat load data within the preset time period;

[0117] The load fluctuation index is obtained by performing weighted processing based on the load operation data and the influencing factor data corresponding to the n time subsequences in combination with the total heat load data.

[0118] It should be noted that the load situation of the heating system will change with time, and different time windows can be selected according to the time series, such as 7 days, 30 days or seasons. In this embodiment, the preset time period is divided into 4 time subsequences according to the season, and then the obtained load operation data and the corresponding influencing factor data are divided according to the 4 time subsequences, and further weighted processing is performed in combination with the total heat load to obtain the load fluctuation index;

[0119] The calculation formula of the load fluctuation index is:

[0120] ;

[0121] in, is the load fluctuation index, is the total heat load data, , , are the water supply temperature data, water supply pressure data, and water supply flow data corresponding to the i-th in the n time subsequences, respectively. , , are the indoor set temperature data, outdoor temperature data, and solar radiation data corresponding to the i-th in the n time subsequences, , is the preset weight factor (the weight factor is obtained by querying the database of the heating system load monitoring platform).

[0122] According to an embodiment of the present invention, extracting the statistical characteristic data of the load operation data and processing to obtain the load shape index includes:

[0123] Extracting statistical characteristic data corresponding to the water supply temperature data, water supply pressure data and water supply flow data;

[0124] The statistical characteristic data includes mean data and standard deviation data;

[0125] The load shape index is obtained by processing the mean data and the standard deviation data corresponding to the water supply temperature data, the water supply pressure data and the water supply flow rate data.

[0126] It should be noted that the statistical characteristic data of the load operation data, including the mean data and standard deviation data corresponding to the water supply temperature, water supply pressure and water supply flow, are extracted, and then the above data are processed to obtain the load shape index;

[0127] The calculation formula of the load shape index is:

[0128] ;

[0129] in, is the load shape index, , , They are the mean data corresponding to the water supply temperature data, water supply pressure data and water supply flow data, , , They are the standard deviation data corresponding to the water supply temperature data, water supply pressure data and water supply flow data, , , , It is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the database of the heating system load monitoring platform).

[0130] According to an embodiment of the present invention, the step of acquiring energy data within the preset time period and processing to obtain a load efficiency characteristic index includes:

[0131] Acquiring energy data within the preset time period, including fuel energy data and heating data;

[0132] The fuel energy data and the heat supply data are processed to obtain a load efficiency characteristic index.

[0133] It should be noted that in order to analyze the load of the heating system more comprehensively and accurately, further obtain the efficiency characteristics of the heating system, obtain energy data within a preset time period, including fuel energy data and heating data, and process to obtain the load efficiency characteristic index;

[0134] The calculation formula of the load efficiency characteristic index is:

[0135] ;

[0136] Where, is the load efficiency characteristic index, , They are fuel energy data and heating data respectively. , It is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the database of the heating system load monitoring platform).

[0137] According to an embodiment of the present invention, the load fluctuation index and the load shape index are processed in combination with the load efficiency characteristic index to obtain a load comprehensive evaluation index, including:

[0138] According to the load fluctuation index and the load shape index, combined with the load efficiency characteristic index, a preset load multi-feature processing model is used for processing to obtain a load comprehensive evaluation index.

[0139] It should be noted that after extracting and processing the multiple features of the heating system, the load fluctuation index, load shape index and load efficiency characteristic index are obtained, and then these major indexes are input into the preset load multi-feature processing model for calculation formula processing to obtain the load comprehensive evaluation index, so as to make a more comprehensive, accurate and reliable load forecast for the heating system;

[0140] The calculation formula of the load comprehensive evaluation index is:

[0141] ;

[0142] in, is the comprehensive evaluation index of load, , , They are load fluctuation index, load shape index, and load efficiency characteristic index. , , , , It is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the database of the heating system load monitoring platform).

[0143] A third aspect of the present invention provides a readable storage medium, which stores a program for a heating system load prediction method based on big data. When the program for a heating system load prediction method based on big data is executed by a processor, the steps of the heating system load prediction method based on big data as described in any one of the above items are implemented.

[0144] The present invention discloses a method, system and medium for load prediction of a heating system based on big data, which obtains load operation data and corresponding influencing factor data of a preset heating system within a preset time period, divides the load operation data and corresponding influencing factor data according to a time series, obtains load operation data and influencing factor data corresponding to n time subsequences, performs weighted processing to obtain a load fluctuation index, extracts statistical characteristic data of the load operation data, processes to obtain a load shape index, obtains energy data within a preset time period, processes to obtain a load efficiency characteristic index, processes the load fluctuation index and the load shape index in combination with the load efficiency characteristic index to obtain a load comprehensive evaluation index, compares the load comprehensive evaluation index with a preset load comprehensive evaluation threshold, and predicts the load of the heating system according to the comparison result, thereby realizing a technology for load prediction of a heating system based on big data.

[0145] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0146] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0147] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0148] Those skilled in the art can understand that: all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions, the aforementioned program can be stored in a readable storage medium, and when the program is executed, it executes the steps of the above method embodiments; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), disks or optical disks, and other media that can store program codes.

[0149] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

Claims

1. A heating system load prediction method based on big data, characterized in that: The following steps are involved: Obtaining load operation data of a preset heating system within a preset time period and corresponding influencing factor data; The load operation data and the corresponding influencing factor data are divided according to the time series to obtain the load operation data and the influencing factor data corresponding to n time subsequences, and the load fluctuation index is obtained by weighted processing; Extracting statistical characteristic data of the load operation data, and processing to obtain a load shape index; Acquiring energy data within the preset time period, and processing to obtain a load efficiency characteristic index; According to the load fluctuation index and the load shape index, combined with the load efficiency characteristic index, a comprehensive load evaluation index is obtained; Comparing the load comprehensive evaluation index with a preset load comprehensive evaluation threshold, and forecasting the heating system load according to the comparison result; The calculation formula of the load fluctuation index is: ； in, is the load fluctuation index, is the total heat load data, , , are the water supply temperature data, water supply pressure data, and water supply flow data corresponding to the i-th in the n time subsequences, respectively. , , are the indoor set temperature data, outdoor temperature data, and solar radiation data corresponding to the i-th in the n time subsequences, , is the preset weight factor; The calculation formula of the load shape index is: ； in, is the load shape index, , , They are the mean data corresponding to the water supply temperature data, water supply pressure data and water supply flow data, , , They are the standard deviation data corresponding to the water supply temperature data, water supply pressure data and water supply flow data, , , , is the preset characteristic coefficient; The calculation formula of the load efficiency characteristic index is: ； Where, is the load efficiency characteristic index, , They are fuel energy data and heating data respectively. , is the preset characteristic coefficient; The calculation formula of the load comprehensive evaluation index is: ； in, is the comprehensive evaluation index of load, , , They are load fluctuation index, load shape index, and load efficiency characteristic index. , , , , is the preset characteristic coefficient.

2. The method for predicting heating system load based on big data according to claim 1, characterized in that: The obtaining of the load operation data of the preset heating system in the preset time period and the corresponding influencing factor data comprises: obtaining the load operation data of the preset heating system in the preset time period and the corresponding influencing factor data; The load operation data includes water supply temperature data, water supply pressure data and water supply flow data; The influencing factor data include indoor set temperature data, outdoor temperature data and solar radiation data.

3. The method for predicting heating system load based on big data according to claim 2 is characterized in that: The step of dividing the load operation data and the corresponding influencing factor data according to the time series to obtain the load operation data and the influencing factor data corresponding to n time subsequences, and performing weighted processing to obtain the load fluctuation index comprises: processing the preset time period according to the time series to obtain n time subsequences; Segment the load operation data and the corresponding influencing factor data according to the n time subsequences to obtain the load operation data and the influencing factor data corresponding to the n time subsequences; Obtaining total heat load data within the preset time period; The load fluctuation index is obtained by performing weighted processing based on the load operation data and the influencing factor data corresponding to the n time subsequences in combination with the total heat load data.

4. The method for predicting heating system load based on big data according to claim 3 is characterized in that: The step of extracting the statistical characteristic data of the load operation data and processing to obtain the load shape index includes: extracting the statistical characteristic data corresponding to the water supply temperature data, the water supply pressure data and the water supply flow rate data; The statistical characteristic data includes mean data and standard deviation data; The load shape index is obtained by processing the mean data and the standard deviation data corresponding to the water supply temperature data, the water supply pressure data and the water supply flow rate data.

5. The method for predicting heating system load based on big data according to claim 4 is characterized in that: The step of acquiring the energy data within the preset time period and processing to obtain the load efficiency characteristic index comprises: acquiring the energy data within the preset time period, including fuel energy data and heating data; The fuel energy data and the heat supply data are processed to obtain a load efficiency characteristic index.

6. The method for predicting heating system load based on big data according to claim 5 is characterized in that: The process of processing the load fluctuation index and the load shape index in combination with the load efficiency characteristic index to obtain a load comprehensive evaluation index includes: processing the load fluctuation index and the load shape index in combination with the load efficiency characteristic index through a preset load multi-feature processing model to obtain a load comprehensive evaluation index.

7. A heating system load forecasting system based on big data, characterized in that: The system comprises: a memory and a processor, wherein the memory comprises a program of a method for predicting the load of a heating system based on big data, and when the program of the method for predicting the load of a heating system based on big data is executed by the processor, the following steps are implemented: obtaining load operation data of a preset heating system within a preset time period and corresponding influencing factor data; The load operation data and the corresponding influencing factor data are divided according to the time series to obtain the load operation data and the influencing factor data corresponding to n time subsequences, and the load fluctuation index is obtained by weighted processing; Extracting statistical characteristic data of the load operation data, and processing to obtain a load shape index; Acquiring energy data within the preset time period, and processing to obtain a load efficiency characteristic index; According to the load fluctuation index and the load shape index, combined with the load efficiency characteristic index, a comprehensive load evaluation index is obtained; Comparing the load comprehensive evaluation index with a preset load comprehensive evaluation threshold, and forecasting the heating system load according to the comparison result; The calculation formula of the load fluctuation index is: ； in, is the load fluctuation index, is the total heat load data, , , are the water supply temperature data, water supply pressure data, and water supply flow data corresponding to the i-th in the n time subsequences, respectively. , , are the indoor set temperature data, outdoor temperature data, and solar radiation data corresponding to the i-th in the n time subsequences, , is the preset weight factor; The calculation formula of the load shape index is: ； in, is the load shape index, , , They are the mean data corresponding to the water supply temperature data, water supply pressure data and water supply flow data, , , They are the standard deviation data corresponding to the water supply temperature data, water supply pressure data and water supply flow data, , , , is the preset characteristic coefficient; The calculation formula of the load efficiency characteristic index is: ； Where, is the load efficiency characteristic index, , They are fuel energy data and heating data respectively. , is the preset characteristic coefficient; The calculation formula of the load comprehensive evaluation index is: ； in, is the comprehensive evaluation index of load, , , They are load fluctuation index, load shape index, and load efficiency characteristic index. , , , , is the preset characteristic coefficient.

8. The heating system load prediction system based on big data according to claim 7 is characterized in that: The obtaining of the load operation data of the preset heating system in the preset time period and the corresponding influencing factor data comprises: obtaining the load operation data of the preset heating system in the preset time period and the corresponding influencing factor data; The load operation data includes water supply temperature data, water supply pressure data and water supply flow data; The influencing factor data include indoor set temperature data, outdoor temperature data and solar radiation data.

9. The heating system load prediction system based on big data according to claim 8, characterized in that: The step of dividing the load operation data and the corresponding influencing factor data according to the time series to obtain the load operation data and the influencing factor data corresponding to n time subsequences, and performing weighted processing to obtain the load fluctuation index comprises: processing the preset time period according to the time series to obtain n time subsequences; Segment the load operation data and the corresponding influencing factor data according to the n time subsequences to obtain the load operation data and the influencing factor data corresponding to the n time subsequences; Obtaining total heat load data within the preset time period; The load fluctuation index is obtained by performing weighted processing based on the load operation data and the influencing factor data corresponding to the n time subsequences in combination with the total heat load data.

10. A computer-readable storage medium, characterized in that: The computer-readable storage medium includes a heating system load prediction method program based on big data. When the heating system load prediction method program based on big data is executed by a processor, the steps of the heating system load prediction method based on big data as described in any one of claims 1 to 6 are implemented.

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