Air source heat pump system energy consumption prediction method

Abstract

The invention discloses an energy consumption prediction method for an air source heat pump system, which can establish a detailed matrix table by monitoring historical operation data of the air source heat pump system and extracting key working condition parameters, and the matrix table covers the relationship between parameters such as outdoor temperature and water supply temperature and indexes such as energy consumption, heat indexes and heat pump COP. A solid basis is provided for subsequent energy consumption prediction; by combining the collected meteorological prediction data, analyzing the external environment condition of the future time period, predicting the energy consumption performance of the air source heat pump system under different temperatures and working conditions, optimizing the energy use, adjusting the system operation strategy in advance, and reducing the energy consumption cost; and parameters such as the thermal index and the COP value are predicted, so that a system manager or operation and maintenance personnel can be helped to formulate a more effective operation strategy.

Description

Technical Field

[0001] The present invention relates to the field of new energy technology, and in particular to a method for predicting energy consumption of an air source heat pump system. Background Art

[0002] With the popularity of air source heat pump units, users are paying more and more attention to the energy consumption of the units. Predicting the energy consumption of air source heat pump systems can reduce total energy consumption and operating costs. However, although the existing technology for predicting the energy consumption of air source heat pumps can collect historical operating data of air source heat pump systems, including operating parameters such as water supply temperature and outdoor temperature, the analysis of these data is relatively simple and rough, and fails to fully explore the potential information and rules in the historical data. The existing technology often ignores external environmental factors (such as meteorological forecast data) when predicting energy consumption, or simply takes them into consideration, and fails to achieve deep integration and comprehensive analysis with historical data. Summary of the invention

[0003] The purpose of the present invention is to provide an air source heat pump system energy consumption prediction method to solve the above technical problems pointed out in the prior art.

[0004] The present invention provides an air source heat pump system energy consumption prediction method, comprising the following steps:

[0005] Monitor the historical operation data of the air source heat pump system, and extract the operating parameters and heating area of ​​the air source heat pump system; extract the operating data according to the operating parameters; establish a square matrix table based on the operating data and the heating area;

[0006] Collect and obtain meteorological forecast data; analyze and predict according to the meteorological forecast data, the square matrix table and the heating area to obtain output predicted energy consumption data.

[0007] Preferably, the operating parameters include outdoor temperature and water supply temperature; the outdoor temperature and the water supply temperature correspond to each other according to each time period in the historical operation data;

[0008] The operating condition data includes hourly energy consumption data, hourly heat pump COP data, hourly heat data and hourly heat index data;

[0009] The matrix table includes an hourly total energy consumption matrix table corresponding to outdoor temperature and water supply temperature, an hourly heat matrix table corresponding to outdoor temperature and water supply temperature, an hourly heat pump energy consumption matrix table corresponding to outdoor temperature and water supply temperature, a thermal index matrix table corresponding to outdoor temperature and water supply temperature, and a heat pump COP matrix table corresponding to outdoor temperature and water supply temperature.

[0010] Preferably, the step of analyzing and predicting the meteorological forecast data, the matrix table and the heating area to obtain output forecast energy consumption data comprises the following steps:

[0011] Calculate the average outdoor temperature for each time period according to the weather forecast data;

[0012] Based on the outdoor average temperature of each time period, the water supply temperature of each time period is predicted by comparing and matching the working condition parameters to obtain the water supply temperature of each time period;

[0013] The predicted energy consumption data is obtained by matching the outdoor average temperature in each time period with the water supply temperature in each time period and calculating in combination with the heating area.

[0014] Preferably, the predicted energy consumption data includes total energy consumption by time period, thermal index by time period, COP value by time period, and load proportion by time period.

[0015] Preferably, the establishing of the square matrix table based on the working condition data and the heating area comprises the following steps:

[0016] Performing denoising and padding processing on the operating condition data to obtain denoised operating condition data;

[0017] The denoised operating condition data includes denoised hourly energy consumption data, denoised hourly heat pump COP data, denoised hourly heat data and denoised hourly heat index data;

[0018] The denoised working condition data is sorted according to time periods to obtain water supply temperature values ​​for the time periods;

[0019] Based on the outdoor temperature of each time period and the water supply temperature value of the time period in the operating condition parameter, the water supply temperature value of each time period corresponding to the outdoor temperature of each time period is matched and screened;

[0020] The water supply temperature values ​​of each time period include the maximum water supply temperature of each time period, the average water supply temperature of each time period, and the minimum water supply temperature of each time period;

[0021] A square matrix table is established based on the water supply temperature value of each time period, the outdoor temperature of each time period, the hourly energy consumption data, the hourly heat data and the heating area.

[0022] Preferably, the square matrix table is established based on the water supply temperature value of each time period, the outdoor temperature of each time period, the hourly energy consumption data, the hourly heat data and the heating area, including the following steps:

[0023] Calculate and obtain hourly heat index data based on the hourly heat data and the heating area;

[0024] Calculate and obtain the hourly heat pump COP value based on the hourly heat data and the hourly heat pump energy consumption data;

[0025] Based on the water supply temperature value of each time period, the outdoor temperature of each time period and the hourly heat pump energy consumption data, a square matrix table of hourly heat pump energy consumption corresponding to the outdoor temperature and the water supply temperature is established;

[0026] Establishing an hourly heat matrix table corresponding to the outdoor temperature and the water supply temperature based on the water supply temperature value of each time period, the outdoor temperature of each time period and the hourly heat data;

[0027] Establishing a thermal index matrix table corresponding to the outdoor temperature and the water supply temperature based on the water supply temperature value of each time period, the outdoor temperature of each time period and the hourly thermal index data;

[0028] Establishing a heat pump COP matrix table corresponding to the outdoor temperature and the water supply temperature based on the water supply temperature value of each time period, the outdoor temperature of each time period and the hourly heat pump COP value;

[0029] Based on the water supply temperature value of each time period, the outdoor temperature of each time period and the hourly total energy consumption, a square matrix table of hourly total energy consumption corresponding to the outdoor temperature and the water supply temperature is obtained.

[0030] Preferably, the predicted energy consumption data is obtained by matching the outdoor average temperature in each time period with the water supply temperature in each time period and then calculating the predicted energy consumption data in combination with the heating area, including the following steps:

[0031] Extracting the hourly energy consumption value corresponding to the outdoor average temperature from the hourly energy consumption matrix table based on the water supply temperature;

[0032] Obtain the duration of the current outdoor average temperature according to the meteorological forecast data; calculate and obtain the predicted total energy consumption in different time periods based on the duration and the hourly energy consumption value;

[0033] Based on the outdoor average temperature and the water supply temperature, a thermal index value is extracted from the thermal index matrix table; based on the thermal index value and the duration, a predicted time-division thermal index value is calculated;

[0034] Extracting the predicted time-division COP value from the COP value matrix table based on the outdoor average temperature and the water supply temperature;

[0035] Obtain a multi-condition correction table; extract a corrected heat value from the multi-condition correction table based on the outdoor average temperature and the water supply temperature; and calculate and obtain a predicted time-period load ratio based on the corrected heat value and the predicted time-period heat index value.

[0036] Preferably, the calculation method of the predicted total energy consumption by time period is:

[0037] Predicted total energy consumption by time period = hourly energy consumption value × duration;

[0038] The calculation method of the predicted time-division heat index value is as follows:

[0039] Predicted heat index value for each time period = heat index value × duration;

[0040] The calculation method of the predicted load proportion in different time periods is as follows:

[0041] Predicted load proportion in different time periods = predicted heat index in different time periods × project heating area / corrected heat value.

[0042] Preferably, the water supply temperature for each time period is predicted by comparing and matching the outdoor average temperature for each time period with the outdoor temperature and water supply temperature matrix table to obtain the water supply temperature for each time period, including the water supply temperature for each time period obtained by a manual version of daily prediction logic processing method, the water supply temperature for each time period obtained by an automatic version of daily prediction logic processing method, and the water supply temperature for each time period obtained by an automatic version of annual prediction logic processing method.

[0043] Preferably, the manual version of the daily prediction logic processing method to obtain the water supply temperature in each time period includes the following steps:

[0044] Manually query the operating parameters according to the outdoor average temperature in each time period to obtain the water supply temperature in each time period;

[0045] The daily prediction logic automatic version processing method obtains the water supply temperature of each time period, including the following steps:

[0046] Based on the outdoor average temperature, the water supply temperature is obtained by matching and extracting the operating condition parameters;

[0047] The automatic processing method of the annual prediction logic obtains the water supply temperature of each period, including the following steps:

[0048] Get historical heating data;

[0049] Get the forecast time point corresponding to the current weather forecast data;

[0050] The water supply temperature corresponding to each time point is obtained by matching the forecast time point with the historical heating data.

[0051] Compared with the prior art, the embodiments of the present invention have at least the following technical advantages:

[0052] By analyzing the above-mentioned air source heat pump system energy consumption prediction method provided by the present invention, it can be known that in specific applications, by monitoring the historical operation data of the air source heat pump system and extracting key operating parameters, detailed matrix tables can be established, which cover the relationship between parameters such as outdoor temperature and water supply temperature and energy consumption, thermal index, heat pump COP and other indicators; this data foundation can provide a solid basis for subsequent energy consumption prediction; further in the technical scheme, by combining the collected meteorological forecast data, analyzing the external environmental conditions in the future period, predicting the energy consumption performance of the air source heat pump system under different temperatures and working conditions, it is helpful to optimize energy use, adjust the system operation strategy in advance, and reduce energy consumption costs; and through the prediction of parameters such as thermal indicators and COP values, it can help system managers or operation and maintenance personnel to formulate more effective operation strategies; for example, increase the use ratio of the heat pump system in the high-efficiency COP value prediction period to reduce total energy consumption and operating costs; by analyzing historical data and comparing future predictions, the system can discover potential energy efficiency problems or room for improvement, and use these data to guide the adjustment and optimization of the system, improve the overall energy efficiency of the air source heat pump system, and reduce energy waste. BRIEF DESCRIPTION OF THE DRAWINGS

[0053] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0054] Figure 1 A schematic diagram of the overall operation steps of an air source heat pump system energy consumption prediction method provided by the present invention;

[0055] Figure 2 A schematic diagram of a simulation of a square matrix table of hourly total energy consumption corresponding to outdoor temperature and water supply temperature for an air source heat pump system energy consumption prediction method provided by the present invention;

[0056] Figure 3 A schematic diagram of a simulation of an hourly heat matrix table corresponding to outdoor temperature and water supply temperature for an air source heat pump system energy consumption prediction method provided by the present invention;

[0057] Figure 4 A schematic diagram of a simulation of a square matrix table of hourly heat pump energy consumption corresponding to outdoor temperature and water supply temperature in an air source heat pump system energy consumption prediction method provided by the present invention;

[0058] Figure 5 A schematic diagram of a thermal index matrix table simulation corresponding to the outdoor temperature and the water supply temperature of an air source heat pump system energy consumption prediction method provided by the present invention;

[0059] Figure 6 A schematic diagram of a heat pump COP matrix table simulation corresponding to the outdoor temperature and the water supply temperature of an air source heat pump system energy consumption prediction method provided by the present invention;

[0060] Figure 7 A schematic diagram of the operation steps of establishing a square matrix table for an air source heat pump system energy consumption prediction method provided by the present invention;

[0061] Figure 8 A schematic diagram of a simulation of data completion processing of an air source heat pump system energy consumption prediction method provided by the present invention;

[0062] Fig. 9 A schematic diagram of a simulation of an original data table of an air source heat pump system energy consumption prediction method provided by the present invention;

[0063] Fig.10 A schematic diagram of a low-temperature extraction data table simulation of an air source heat pump system energy consumption prediction method provided by the present invention;

[0064] Fig.11 A schematic diagram of a normal extraction data table simulation of an air source heat pump system energy consumption prediction method provided by the present invention;

[0065] Fig.12 A schematic diagram of the operation steps of establishing a square matrix table for an air source heat pump system energy consumption prediction method provided by the present invention;

[0066] Fig.13 A schematic diagram of the operation steps of obtaining output predicted energy consumption data of an air source heat pump system energy consumption prediction method provided by the present invention;

[0067] Fig.14 A schematic diagram of the operating steps for obtaining predicted energy consumption data of an air source heat pump system energy consumption prediction method provided by the present invention. DETAILED DESCRIPTION

[0068] The technical solution of the present invention will be described clearly and completely below in conjunction with the accompanying drawings. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0069] The present invention is further described in detail below through specific embodiments in conjunction with the accompanying drawings.

[0070] like Figure 1 As shown, the present invention proposes an air source heat pump system energy consumption prediction method, comprising the following steps:

[0071] Step S10: monitoring the historical operation data of the air source heat pump system, and extracting the operating parameters and heating area of ​​the air source heat pump system; extracting the operating data according to the operating parameters; and establishing a square matrix table based on the operating data and the heating area;

[0072] It is generally believed that historical operation data is comprehensive data including multiple parameters such as the operating parameters of the air source heat pump system; the operating parameters include outdoor temperature and water supply temperature; the operating parameters involve working parameters in the equipment and external environmental parameters, the working parameters in the equipment are such as water supply temperature; the external environmental parameters are such as outdoor temperature; the outdoor temperature and the water supply temperature correspond to each other according to each time period in the historical operation data (that is, the outdoor temperature in each time period and the water supply temperature in each time period correspond to each other in the historical operation data);

[0073] The operating condition data includes hourly energy consumption data, hourly heat pump COP data, hourly heat data and hourly heat index data;

[0074] The matrix table includes a matrix table of hourly total energy consumption corresponding to outdoor temperature and water supply temperature (such as Figure 2 ), the hourly heat matrix table corresponding to the outdoor temperature and the water supply temperature (as shown in Figure 3 ), the hourly heat pump energy consumption matrix table corresponding to the outdoor temperature and the water supply temperature (as shown in Figure 4 As shown), the thermal index matrix table corresponding to the outdoor temperature and the water supply temperature (hourly heat / heating area, such as Figure 5 As shown) and the heat pump COP matrix table corresponding to the outdoor temperature and the water supply temperature (as shown Figure 6 shown);

[0075] Step S20: Acquire meteorological forecast data (i.e., the meteorological forecast data can be captured from a meteorological data platform or a meteorological forecast website); perform analysis and forecasting based on the meteorological forecast data, the square matrix table, and the heating area to obtain output forecast energy consumption data;

[0076] The predicted energy consumption data includes predicted total energy consumption by time period, predicted thermal index by time period, predicted COP value by time period, and predicted load proportion by time period;

[0077] It should be noted that the above-mentioned embodiment of the present application can establish detailed matrix tables by monitoring the historical operating data of the air source heat pump system and extracting key operating parameters. These matrix tables cover the relationship between parameters such as outdoor temperature and water supply temperature and indicators such as energy consumption, thermal indicators, and heat pump COP; this data foundation can provide a solid basis for subsequent energy consumption forecasts; further in the technical solution, by combining the collected meteorological forecast data, analyzing the external environmental conditions in the future period, and predicting the energy consumption performance of the air source heat pump system under different temperatures and working conditions, it is helpful to optimize energy use, adjust the system operation strategy in advance, and reduce energy consumption costs; and through the prediction of parameters such as thermal indicators and COP values, it can help system managers or operation and maintenance personnel to formulate more effective operation strategies; for example, increase the use ratio of the heat pump system during the high-efficiency COP value prediction period to reduce total energy consumption and operating costs; by analyzing historical data and comparing future predictions, the system can discover potential energy efficiency problems or room for improvement. Using these data, it can guide the adjustment and optimization of the system, improve the overall energy efficiency of the air source heat pump system, and reduce energy waste.

[0078] Specifically, Figure 7 As shown, in step S10, a square matrix table is established based on the working condition data and the heating area, including the following operation steps:

[0079] Step S11: performing denoising and padding processing on the operating condition data to obtain denoised operating condition data;

[0080] The denoised operating condition data includes denoised hourly energy consumption data, denoised hourly heat pump COP data, denoised hourly heat data and denoised hourly heat index data;

[0081] It should be noted that the above-mentioned denoising and padding processing is to first denoise the operating condition data and then pad the operating condition data; the above-mentioned denoising processing refers to deleting abnormal data in the operating condition data; in the established square matrix table, both the horizontal and vertical columns show an increasing trend, and there are operating condition data that are outside the trend. The embodiment of the present application regards the data outside the trend as abnormal data and deletes them. This is because the data points outside the trend may be caused by equipment failure, measurement errors or other abnormal conditions, which will have a negative impact on the establishment of the model and the prediction results; by deleting these abnormal data, noise and interference can be reduced, thereby improving the accuracy and reliability of the model; and data points outside the trend may cause the model to deviate during prediction, affecting the accuracy of system design and optimization decisions. Deleting these abnormal data helps to ensure the stability and consistency of the model, and ensure the effectiveness of the analysis and the rationality of the conclusions;

[0082] The above-mentioned filling process is performed by filling in the increasing average value. Specifically, the blank data between two numbers are filled in by using the increasing average principle. The blank data before or after the data are filled in by the increasing average value of the existing data in this column or row. This is because in the process of establishing the square matrix table, if there are missing data in some positions (i.e. blank data), these blank data may destroy the continuity and incrementality of the data. By using the increasing average value for filling, the increasing trend of the data sequence can be maintained, making the overall data consistent and comparable. At the same time, missing data will lead to uncertainty about the data at that position, especially when conducting data analysis and decision-making, this uncertainty will affect the reliability of the results. By filling in the increasing average value, this uncertainty can be reduced, making the analysis and application of the data more stable and reliable. Filling in the data can make the data set more complete and standardized, which is conducive to subsequent data analysis, model building and decision support. This processing method can simplify the complexity of data preprocessing, allowing analysts or decision makers to focus more on the actual trends and problems behind the data.

[0083] For example: Figure 8 As shown, the black words in the table below are the actual measured values ​​extracted from the actual operation data. This value should meet the requirement that when the outdoor temperature is the same, the higher the execution temperature, the higher the energy consumption value; when the execution temperature is the same, the lower the outdoor temperature, the higher the energy consumption value; the numbers in the gray background in the figure below do not meet this rule and are considered as abnormal data and deleted; the numbers in the gray plus elliptical background are the supplementary data and between the two numbers, using the increasing average value, so (the previous number + the next number) / 2 is equal to the supplementary data here; the numbers in the gray background are also supplementary values, and the supplementary value (the previous number + the next number) / (3+1) is incremented and filled with the supplementary value at intervals based on this calculated value; the values ​​in the hourly energy consumption, hourly heat pump energy consumption, and hourly heat in the square matrix table are sorted in this way, and the heat pump COP square matrix and the thermal index square matrix are square matrices generated by calculation formulas;

[0084] The above-mentioned denoising and padding processes are both existing technologies and will not be described in detail in this application.

[0085] Step S12: sorting the denoised working condition data according to time periods to obtain water supply temperature values ​​for each time period;

[0086] It should be noted that the above embodiment of the present application divides the denoised working condition data into time periods of heating, normal and low temperature to obtain the temperature data of each time period;

[0087] The above time periods are arranged according to the demand for heating. For example, the school starts heating at 7:00 in the morning and stops heating at 17:00 in the evening. This is the normal heating period. After the heating is stopped, a certain indoor temperature must be maintained to prevent the water pipes from freezing and cracking. It is usually from 17:00 to 4:00. This time period is the low temperature period. The indoor temperature is low at low temperatures. Therefore, in order to meet the heating demand at 7:00, the indoor temperature must be quickly increased in advance with a large load. This time period is generally 4:00 to 7:00, which is defined as the heating period.

[0088] The original data table and extracted data are shown in Fig. 9 , Fig.10 , Fig.11 ;

[0089] Step S13: matching and screening the outdoor temperature of each time period with the water supply temperature value of each time period in the operating condition parameter to obtain the water supply temperature value of each time period corresponding to the outdoor temperature of each time period;

[0090] The water supply temperature values ​​of each time period include the maximum water supply temperature of each time period, the average water supply temperature of each time period, and the minimum water supply temperature of each time period;

[0091] Step S14: establishing a square matrix table based on the water supply temperature value of each time period, the outdoor temperature of each time period, the hourly energy consumption data, the hourly heat data and the heating area;

[0092] Specifically, Fig.12 As shown, in step S14, a square matrix table is established based on the water supply temperature value of each time period, the outdoor temperature of each time period, the hourly energy consumption data, the hourly heat data and the heating area, including the following operation steps:

[0093] Step S141: Calculating and acquiring hourly heat index data based on the hourly heat data and the heating area;

[0094] Step S142: Calculate and obtain the hourly heat pump COP value based on the hourly heat data and the hourly heat pump energy consumption data;

[0095] Step S143: establishing an hourly heat pump energy consumption matrix table corresponding to the outdoor temperature and the water supply temperature based on the water supply temperature value of each time period, the outdoor temperature of each time period and the hourly heat pump energy consumption data;

[0096] Step S144: establishing an hourly heat matrix table corresponding to the outdoor temperature and the water supply temperature based on the water supply temperature value of each time period, the outdoor temperature of each time period and the hourly heat data;

[0097] Step S145: establishing a thermal index matrix table corresponding to the outdoor temperature and the water supply temperature based on the water supply temperature value of each time period, the outdoor temperature of each time period and the hourly thermal index data;

[0098] Step S146: establishing a heat pump COP matrix table corresponding to the outdoor temperature and the water supply temperature based on the water supply temperature value of each time period, the outdoor temperature of each time period and the hourly heat pump COP value;

[0099] Step S147: based on the water supply temperature value of each time period, the outdoor temperature of each time period and the hourly total energy consumption, a square matrix table of hourly total energy consumption corresponding to the outdoor temperature and the water supply temperature is obtained.

[0100] It should be noted that the above-mentioned embodiment of the present application provides a large amount of real-time data and analysis results by establishing a detailed matrix table, so that system managers can make more accurate decisions and adjustments based on the data; by analyzing and optimizing the system's energy efficiency performance under different temperature conditions, the overall energy efficiency of the air source heat pump system can be effectively improved.

[0101] Specifically, Fig.13 As shown, in step S20, the forecast energy consumption data is outputted by analyzing and forecasting the meteorological forecast data, the square matrix table and the heating area, including the following operation steps:

[0102] Step S21: Calculating the average outdoor temperature of each time period according to the weather forecast data;

[0103] Step S22: Based on the outdoor average temperature of each time period, the water supply temperature of each time period is predicted by comparing and matching the working condition parameters to obtain the water supply temperature of each time period;

[0104] It should be noted that because the outdoor temperature and the water supply temperature in the operating condition parameters correspond to each other in time periods, the embodiment of the present application uses the outdoor average temperature of each time period in the meteorological forecast data as an index to compare and match the outdoor average temperature of each time period in the meteorological forecast data with the water supply temperature of each time period in the operating condition parameters.

[0105] Step S23: The predicted energy consumption data is calculated based on the heating area after matching the outdoor average temperature of each time period with the water supply temperature of each time period;

[0106] The predicted energy consumption data includes total energy consumption by time period, thermal index by time period, COP value by time period, and load proportion by time period;

[0107] It should be noted that the total energy consumption by time period: after the outdoor average temperature and water supply temperature working conditions are clear, the system automatically extracts the hourly energy consumption value corresponding to the working conditions from the hourly energy consumption matrix table and then multiplies it by the number of hours corresponding to this time period; thermal index by time period: after the outdoor average temperature and water supply temperature working conditions are clear, the system automatically extracts the thermal index value corresponding to the working conditions from the thermal index matrix table; COP value by time period: after the outdoor average temperature and water supply temperature working conditions are clear, the system automatically extracts the COP value corresponding to the working conditions from the COP value matrix table; load proportion by time period: after the outdoor average temperature and water supply temperature working conditions are clear, the system automatically extracts the heat value corresponding to the working conditions from the manufacturer's multi-condition correction table, and then uses the actual heat index corresponding to this period * project heating area * corresponding number of hours / correction table working conditions to match the heat value.

[0108] Specifically, in step S22, the outdoor average temperature of each time period is compared and matched with the outdoor temperature and water supply temperature matrix table to realize the prediction of the water supply temperature of each time period, and the water supply temperature of each time period is obtained, including the water supply temperature of each time period obtained by the manual version of the daily prediction logic processing method, the water supply temperature of each time period obtained by the automatic version of the daily prediction logic processing method, and the water supply temperature of each time period obtained by the automatic version of the annual prediction logic processing method;

[0109] The manual version of the daily prediction logic processing method to obtain the water supply temperature in each time period includes the following steps:

[0110] Manually query the operating parameters according to the outdoor average temperature in each time period to obtain the water supply temperature in each time period;

[0111] Specifically, the manual query is performed manually according to weather conditions, specifically, the minimum water supply temperature is selected on a sunny day, the average water supply temperature is selected on a cloudy day, and the highest water supply temperature is selected in inclement weather that cannot exceed the maximum water supply temperature.

[0112] The daily prediction logic automatic version processing method obtains the water supply temperature of each time period, including the following steps:

[0113] Based on the outdoor average temperature, the water supply temperature is obtained by matching and extracting the operating condition parameters;

[0114] It should be noted that the above-mentioned embodiment of the present application collects meteorological forecast data, and after importing hourly weather data at corresponding times, the system automatically outputs the average outdoor temperature value and the average water supply temperature value (the average value is used) for each time period.

[0115] The automatic processing method of the annual prediction logic obtains the water supply temperature of each period, including the following steps:

[0116] Get historical heating data;

[0117] The historical function data refers to the water supply temperature and the time points corresponding to the water supply temperature and the outdoor temperature data corresponding to each time point in the historical time period of the project in the embodiment of the present application (for example, from 2018 to 2023);

[0118] Get the forecast time point corresponding to the current weather forecast data;

[0119] According to the forecast time point, the water supply temperature corresponding to each time point is obtained by matching the historical heating data;

[0120] It should be noted that the above-mentioned embodiment of the present application respectively extracts the hourly time points of heating, normal and low temperature of this project from 2018 to 2023, and after importing the hourly outdoor temperature data corresponding to each time point, the system will automatically generate the water supply temperature corresponding to each time point of the current meteorological forecast data (the average value is used).

[0121] Specifically, Fig.14 As shown, in step S23, the predicted energy consumption data is obtained by matching the outdoor average temperature of each time period with the water supply temperature of each time period and combining the heating area, including the following operation steps:

[0122] Step S231: extracting the hourly energy consumption value corresponding to the outdoor average temperature from the hourly energy consumption matrix table based on the water supply temperature;

[0123] It should be noted that the hourly energy consumption value is extracted from the hourly energy consumption matrix table using the Excel first extraction function. The Excel first extraction function is exemplified as:

[0124] INDEX(Hourly Energy Consumption Matrix Table!$C$3:$AD$29,VLOOKUP(E4,Hourly Energy Consumption Matrix Table!$C$36:$D$85,2,0),VLOOKUP(F4,Hourly Energy Consumption Matrix Table!$F$36:$G$86,2,0))*B4; The Excel first extraction function is a data extraction function for Excel table applications developed by Microsoft;

[0125] Step S232: obtaining the duration of the current outdoor average temperature according to the meteorological forecast data; and obtaining the predicted total energy consumption by time period based on the duration and the hourly energy consumption value;

[0126] The calculation method of the predicted total energy consumption in different time periods is:

[0127] Predicted total energy consumption by time period = hourly energy consumption value × duration;

[0128] Step S233: extracting a thermal index value from the thermal index matrix table based on the outdoor average temperature and the water supply temperature; and calculating and obtaining a predicted time-division thermal index value based on the thermal index value and the duration;

[0129] The calculation method of the predicted time-division heat index value is as follows:

[0130] Predicted heat index value for each time period = heat index value × duration;

[0131] Step S234: extracting predicted time-division COP values ​​from the COP value matrix table based on the outdoor average temperature and the water supply temperature;

[0132] Step S235: obtaining a multi-condition correction table; extracting a corrected heat value from the multi-condition correction table based on the outdoor average temperature and the water supply temperature; calculating and obtaining a predicted time-division load ratio based on the corrected heat value and the predicted time-division heat index value;

[0133] The calculation method of the predicted load proportion in different time periods is as follows:

[0134] Predicted load proportion in different time periods = predicted heat index in different time periods × project heating area / corrected heat value;

[0135] It should be noted that the above-mentioned multi-condition correction table is provided by the manufacturer for the corresponding equipment rated heat values ​​under different outdoor temperatures and different water supply temperatures (the single-degree manufacturer heat pump heat matrix table is filled with data according to the matrix data supplement logic); the correction table provided by the manufacturer is used to calculate the load proportion, and when the load proportion exceeds 100%, an early warning is issued to guide strategy adjustments;

[0136] The above-mentioned embodiment of the present application uses the index and search functions of Excel to extract the corresponding hourly energy consumption value from the pre-established hourly energy consumption matrix table according to the outdoor average temperature and the water supply temperature, which intuitively reflects the energy consumption performance of the air source heat pump system under specific working conditions; using the outdoor average temperature duration in the meteorological forecast data, the hourly energy consumption value extracted in step S231 is multiplied by the duration to calculate the total energy consumption of the predicted time period, providing the system with the expected energy consumption in the future time period; according to the outdoor average temperature and the water supply temperature, the corresponding thermal index value is obtained from the thermal index matrix table and then the predicted thermal index value of the predicted time period is calculated; from the pre-established COP value matrix table, the corresponding COP value is extracted according to the outdoor average temperature and the water supply temperature. COP (Coefficient of Performance) is an important indicator for measuring the energy efficiency of heat pump systems. The COP value is extracted from the COP matrix table using the outdoor temperature and the water supply temperature, that is, the predicted COP value for each time period is obtained. By obtaining and applying a multi-condition correction table, which provides the rated heat value of the equipment under different outdoor temperature and water supply temperature conditions, the load proportion for each time period is calculated based on the corrected heat value and the predicted time period heat index value calculated in step S233, indicating whether the current predicted workload of the system is reasonable or whether the operation strategy needs to be adjusted. By comprehensively considering factors such as outdoor temperature and water supply temperature and combining the matrix table established with historical data, the system can provide accurate energy consumption prediction. The predicted time period heat index and COP value can help decision makers adjust the operation strategy of the air source heat pump system to achieve optimal efficiency. The calculation and analysis of the predicted time period load proportion, combined with the data in the correction table, can provide early warning of possible load overload. The Excel functions and pre-established matrix tables used in the scheme provide an intuitive and easy-to-understand data processing and analysis method.

[0137] In summary, the energy consumption prediction method of an air source heat pump system proposed in the example of the present invention can establish detailed matrix tables by monitoring the historical operation data of the air source heat pump system and extracting key operating parameters. These matrix tables cover the relationship between parameters such as outdoor temperature and water supply temperature and energy consumption, thermal index, heat pump COP and other indicators; this data foundation can provide a solid basis for subsequent energy consumption prediction; in a further technical solution, by combining the collected meteorological forecast data, analyzing the external environmental conditions in the future period, and predicting the energy consumption performance of the air source heat pump system under different temperatures and working conditions, it is helpful to optimize energy use, adjust the system operation strategy in advance, and reduce energy consumption costs; and through the prediction of parameters such as thermal indicators and COP values, it can help system managers or operation and maintenance personnel to formulate more effective operation strategies; for example, increase the use ratio of the heat pump system during the high-efficiency COP value prediction period to reduce total energy consumption and operating costs; through the analysis of historical data and comparison of future predictions, the system can discover potential energy efficiency problems or room for improvement. Using these data, it can guide the adjustment and optimization of the system, improve the overall energy efficiency of the air source heat pump system, and reduce energy waste.

[0138] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, rather than to limit it. A person skilled in the art may modify the technical solutions described in the above embodiments, or replace part or all of the technical features therein with equivalents. However, these modifications or replacements do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention.

Claims

1. A method for predicting energy consumption of an air source heat pump system, characterized in that: The steps are as follows: Monitor the historical operation data of the air source heat pump system, and extract the operating parameters and heating area of ​​the air source heat pump system; extract the operating data according to the operating parameters; and establish a square matrix table based on the operating data and the heating area; Collect and obtain meteorological forecast data; analyze and predict according to the meteorological forecast data, the square matrix table and the heating area to obtain output predicted energy consumption data.

2. The method for predicting energy consumption of an air source heat pump system according to claim 1, characterized in that: The operating parameters include outdoor temperature and water supply temperature; the outdoor temperature and the water supply temperature correspond to each other according to each time period in the historical operation data; The operating condition data includes hourly energy consumption data, hourly heat pump COP data, hourly heat data and hourly heat index data; The matrix table includes an hourly total energy consumption matrix table corresponding to outdoor temperature and water supply temperature, an hourly heat matrix table corresponding to outdoor temperature and water supply temperature, an hourly heat pump energy consumption matrix table corresponding to outdoor temperature and water supply temperature, a thermal index matrix table corresponding to outdoor temperature and water supply temperature, and a heat pump COP matrix table corresponding to outdoor temperature and water supply temperature.

3. The method for predicting energy consumption of an air source heat pump system according to claim 2, characterized in that: The step of analyzing and predicting the meteorological forecast data, the square matrix table, and the heating area to obtain output forecast energy consumption data includes the following steps: Calculate the average outdoor temperature for each time period according to the weather forecast data; Based on the outdoor average temperature of each time period, the water supply temperature of each time period is predicted by comparing and matching the working condition parameters to obtain the water supply temperature of each time period; The predicted energy consumption data is obtained by matching the outdoor average temperature in each time period with the water supply temperature in each time period and calculating in combination with the heating area.

4. The method for predicting energy consumption of an air source heat pump system according to claim 3, characterized in that: The predicted energy consumption data includes total energy consumption by time period, thermal index by time period, COP value by time period, and load proportion by time period.

5. The method for predicting energy consumption of an air source heat pump system according to claim 4, characterized in that: The step of establishing a square matrix table based on the working condition data and the heating area includes the following steps: Performing denoising and padding processing on the operating condition data to obtain denoised operating condition data; The denoised operating condition data includes denoised hourly energy consumption data, denoised hourly heat pump COP data, denoised hourly heat data and denoised hourly heat index data; The denoised working condition data is sorted according to time periods to obtain water supply temperature values ​​for the time periods; Based on the outdoor temperature of each time period and the water supply temperature value of the time period in the operating condition parameter, the water supply temperature value of each time period corresponding to the outdoor temperature of each time period is matched and screened; The water supply temperature values ​​of each time period include the maximum water supply temperature of each time period, the average water supply temperature of each time period, and the minimum water supply temperature of each time period; A square matrix table is established based on the water supply temperature value of each time period, the outdoor temperature of each time period, the hourly energy consumption data, the hourly heat data and the heating area.

6. The method for predicting energy consumption of an air source heat pump system according to claim 5, characterized in that: The square matrix table is established based on the water supply temperature value of each time period, the outdoor temperature of each time period, the hourly energy consumption data, the hourly heat data and the heating area, including the following steps: Calculate and obtain hourly heat index data based on the hourly heat data and the heating area; Calculate and obtain the hourly heat pump COP value based on the hourly heat data and the hourly heat pump energy consumption data; Based on the water supply temperature value of each time period, the outdoor temperature of each time period and the hourly heat pump energy consumption data, a square matrix table of hourly heat pump energy consumption corresponding to the outdoor temperature and the water supply temperature is established; Establishing an hourly heat matrix table corresponding to the outdoor temperature and the water supply temperature based on the water supply temperature value of each time period, the outdoor temperature of each time period and the hourly heat data; Establishing a thermal index matrix table corresponding to the outdoor temperature and the water supply temperature based on the water supply temperature value of each time period, the outdoor temperature of each time period and the hourly thermal index data; Establishing a heat pump COP matrix table corresponding to the outdoor temperature and the water supply temperature based on the water supply temperature value of each time period, the outdoor temperature of each time period and the hourly heat pump COP value; Based on the water supply temperature value of each time period, the outdoor temperature of each time period and the hourly total energy consumption, a square matrix table of hourly total energy consumption corresponding to the outdoor temperature and the water supply temperature is obtained.

7. The method for predicting energy consumption of an air source heat pump system according to claim 6, characterized in that: The method of matching the outdoor average temperature in each time period with the water supply temperature in each time period and calculating the predicted energy consumption data in combination with the heating area includes the following steps: Extracting the hourly energy consumption value corresponding to the outdoor average temperature from the hourly energy consumption matrix table based on the water supply temperature; Obtaining the duration of the current average outdoor temperature according to the meteorological forecast data; Calculate and obtain the predicted total energy consumption in each time period based on the duration and the hourly energy consumption value; Extracting a thermal index value from the thermal index matrix table based on the outdoor average temperature and the water supply temperature; Calculate and obtain the predicted time-division thermal index value based on the thermal index value and the duration; Extracting the predicted time-division COP value from the COP value matrix table based on the outdoor average temperature and the water supply temperature; Obtain multi-condition correction table; Extracting a corrected calorific value from the multi-condition correction table based on the outdoor average temperature and the water supply temperature; The predicted time-division load ratio is obtained by calculation based on the corrected heat value and the predicted time-division heat index value.

8. The method for predicting energy consumption of an air source heat pump system according to claim 7, characterized in that: The calculation method of the predicted total energy consumption in different time periods is: Predicted total energy consumption by time period = hourly energy consumption value × duration; The calculation method of the predicted time-division heat index value is as follows: Predicted heat index value for each time period = heat index value × duration; The calculation method of the predicted load proportion in different time periods is as follows: Predicted load proportion in different time periods = predicted heat index in different time periods × project heating area / corrected heat value.

9. The method for predicting energy consumption of an air source heat pump system according to claim 8, characterized in that: The water supply temperature for each time period is predicted by comparing and matching the outdoor average temperature for each time period with the outdoor temperature and water supply temperature matrix table, and the water supply temperature for each time period is obtained, including the water supply temperature for each time period obtained by the manual version of the daily prediction logic processing method, the water supply temperature for each time period obtained by the automatic version of the daily prediction logic processing method, and the water supply temperature for each time period obtained by the automatic version of the annual prediction logic processing method.

10. The method for predicting energy consumption of an air source heat pump system according to claim 9, characterized in that: The manual version of the daily prediction logic processing method to obtain the water supply temperature in each time period includes the following steps: Manually query the operating parameters according to the outdoor average temperature in each time period to obtain the water supply temperature in each time period; The daily prediction logic automatic version processing method obtains the water supply temperature of each time period, including the following steps: Based on the outdoor average temperature, the water supply temperature is obtained by matching and extracting the operating condition parameters; The automatic processing method of the annual prediction logic obtains the water supply temperature of each period, including the following steps: Get historical heating data; Get the forecast time point corresponding to the current weather forecast data; The water supply temperature corresponding to each time point is obtained by matching the forecast time point with the historical heating data.

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