Energy-saving control method and system for heat pump

Abstract

The invention relates to the technical field of heat supply management, and discloses an energy-saving control method and system for a heat pump, and the method comprises the steps: collecting meteorological data in a preset region, judging whether to carry out preheating control or not, and determining a cooling center point and a neighborhood boundary range when the preheating control is judged to be carried out; obtaining forecast data of each point in the neighborhood boundary range; obtaining historical data of each point in a neighborhood boundary range, combining the historical data with the forecast data to obtain a combined data set, and constructing a Gaussian kernel density function to determine cooling alarm information of a cooling center point; the heat storage capacity is determined according to the early warning level, and the real-time temperature change rate before the cooling moment in the preset area is collected to judge whether the heat storage capacity is adjusted or not. Through prediction and intelligent adjustment, the energy-saving and response efficiency of the heat pump system is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of heat supply management, and in particular to an energy-saving control method and system for a heat pump. Background Art

[0002] With the rapid development of heat pump technology, energy saving and efficient control have become the core research directions of modern heat pump systems. As an efficient means of heat energy transmission, heat pump technology uses low-temperature heat from the external environment to provide comfortable temperature control services for buildings.

[0003] However, most current heat pump control technologies are based on ambient temperature sensors and traditional thermostatic adjustment methods, which cannot foresee and effectively adjust when faced with complex meteorological conditions. For example, when meteorological conditions suddenly cool down or there is extreme weather, the heat pump adjusts the valve by detecting the current temperature to ensure that the indoor temperature remains within a comfortable range. Relying on simple temperature thresholds or empirical rules and failing to fully consider the predictive characteristics of meteorological data, the system is unable to predict and respond to meteorological changes in a timely manner, and simply increasing the operating efficiency of the heat pump to maintain the indoor temperature under cooling conditions leads to energy waste.

[0004] Therefore, it is necessary to design an energy-saving control method and system for a heat pump to solve the problems existing in the current technology. Summary of the invention

[0005] In view of this, the present invention proposes an energy-saving control method and system for a heat pump, aiming to solve the problem of lack of predictive regulation leading to regulation lag and energy waste in the current heating based on heat pumps.

[0006] In one aspect, the present invention provides an energy-saving control method for a heat pump, comprising: Collecting meteorological data in a preset area for storage, determining whether to perform preheating control based on the meteorological data, and when it is determined that the preheating control is to be performed, determining a cooling center point based on the meteorological data, and determining a neighborhood boundary range based on the meteorological data of the cooling center point; Obtain forecast data for each point within the neighborhood boundary based on the minute-level grid temperature drop forecast and the CMA-MESO temperature drop forecast; Obtaining historical data of each point within the neighborhood boundary, combining the historical data with forecast data to obtain a combined data set, constructing a Gaussian kernel density function according to the combined data set, drawing a peak density map according to the Gaussian kernel density function, and determining cooling alarm information of the cooling center point according to the peak density map, wherein the cooling alarm information includes a cooling time and a warning level; The heat storage amount is determined according to the warning level, the real-time temperature change rate in the preset area before the temperature drop moment is collected, and whether to adjust the heat storage amount is determined according to the temperature change rate.

[0007] Further, the determining whether to perform preheating control according to the meteorological data includes: Comparing the meteorological data of each point in the preset area with the data threshold respectively, and judging whether to perform preheating control according to the comparison result; the meteorological data includes temperature data, and the data threshold includes a temperature threshold; When the temperature data is less than the temperature threshold, determining to perform the preheating control; When the temperature data is greater than or equal to a temperature threshold, it is determined that the warm-up control is not performed.

[0008] Further, when determining the neighborhood boundary range according to the meteorological data of the cooling center point, it includes: A point in the preset area where the meteorological data is greater than the data threshold is selected as the cooling center point, and the neighborhood boundary range is determined according to the real-time meteorological data of the cooling center point. The neighborhood boundary range is inversely proportional to the real-time meteorological data.

[0009] Further, when obtaining forecast data of each point within the neighborhood boundary based on the minute-level grid temperature drop forecast and the CMA-MESO temperature drop forecast, it includes: Obtain forecast data for the neighborhood boundary range of 0-2 hours based on the minute-level grid temperature drop forecast; Obtain forecast data for the neighborhood boundary range of 2-12 hours based on the CMA-MESO temperature drop forecast; The forecast data of the neighborhood boundary range of 2-12h is converted from resolution Convert to ; The transformed forecast data for the neighborhood boundary range of 2-12 hours are fused with the forecast data for the neighborhood boundary range of 0-2 hours to obtain the forecast data for the neighborhood boundary range of 0-12 hours.

[0010] Furthermore, combining the historical data with the forecast data to obtain a combined data set includes: Obtain the meteorological data of the neighborhood boundary range in the past 3 hours according to the historical data; Obtaining forecast data for the next 3 hours based on the forecast data of the neighborhood boundary range of 0-12 hours; The meteorological data for the past 3 hours are combined with the forecast data for the next 3 hours to obtain the combined data set.

[0011] Further, when constructing a Gaussian kernel density function according to the combined data set, the Gaussian kernel density function is: ; Where n represents the number of data samples in the combined data set, h represents the smoothing bandwidth, and s i represents the i-th data point in the combined data set, and x represents any variable in the overall data; The smoothing bandwidth is calculated by the following formula: ; Among them, h represents the smoothing bandwidth, σ represents the standard deviation of the data in the combined data set, β1 represents the data skewness, β2 represents the data kurtosis, n represents the number of data samples in the combined data set, and k represents the adjustment coefficient; The data skewness is calculated by the following formula: ; The data kurtosis is calculated by the following formula: ; Among them, n represents the number of data samples in the combined data set, s i represents the i-th data point in the combined data set, represents the mean of the data in the combined data set, and σ represents the standard deviation of the data in the combined data set.

[0012] Further, when determining the cooling alarm information of the cooling center point according to the peak density map, it includes: Obtain a meteorological data difference according to the meteorological data corresponding to the highest value of the density estimation value in the peak density map and the data threshold, obtain the cooling time according to the meteorological data corresponding to the highest value of the density estimation value in the peak density map and the forecast data, compare the meteorological data difference with the first preset difference and the second preset difference respectively, and determine the warning level of the cooling center point according to the comparison result; the first preset difference is less than the second preset difference; When the meteorological data difference is less than or equal to a first preset difference, the first cooling level is determined as the warning level; when the meteorological data difference is greater than the first preset difference and less than or equal to a second preset difference, the second cooling level is determined as the warning level; when the meteorological data difference is greater than the second preset difference, the third cooling level is determined as the warning level; the first cooling level indicates that the degree of cooling is higher than the second cooling level, and the second cooling level indicates that the degree of cooling is higher than the third cooling level.

[0013] Further, collecting the real-time temperature change rate in the preset area before the temperature drop moment, and judging whether to adjust the heat storage according to the temperature change rate, includes: The predicted temperature change rate is obtained according to the meteorological data corresponding to the highest value of the density estimation value in the peak density map and the temperature drop time, the temperature change rate is compared with the predicted temperature change rate, and it is determined whether the heat storage is adjusted according to the comparison result.

[0014] Further, judging whether to adjust the heat storage amount according to the comparison result includes: When the temperature change rate is less than the predicted temperature change rate, it is determined that the heat storage amount is adjusted, and an adjustment coefficient is determined according to the absolute value of the difference between the temperature change rate and the predicted temperature change rate to adjust the heat storage amount, and the adjustment coefficient is proportional to the absolute value of the difference; when the temperature change rate is greater than or equal to the predicted temperature change rate, it is determined that the heat storage amount is not adjusted.

[0015] Compared with the prior art, the beneficial effects of the present invention are: through prediction and intelligent adjustment, the energy saving and response efficiency of the heat pump system are improved. By collecting and storing meteorological data, and combining minute-level temperature drop forecasts and historical data, it is possible to accurately judge the temperature drop trend and perform preheating control in advance. Dynamic adjustment based on meteorological data avoids the traditional method of relying on simple temperature thresholds, and can make accurate responses under complex meteorological conditions. The peak density map is constructed by the Gaussian kernel density function, and the temperature drop alarm information is further optimized to ensure that the heat pump adjusts the operation strategy in time before the temperature drop moment to avoid unnecessary energy waste. At the same time, combined with the real-time temperature change rate monitoring, the heat storage amount is finely adjusted so that the heat pump operates at the optimal energy efficiency state. The heat pump's ability to adapt to sudden weather changes is improved, energy consumption is reduced, and the comfort of indoor temperature control is ensured.

[0016] On the other hand, the present application also provides an energy-saving control system for a heat pump, which is used to apply the above energy-saving control method for a heat pump, including: A heat storage module configured to store a high temperature medium; The control module includes a collection unit, a processing unit, a parsing unit and an adjustment unit, wherein: The acquisition unit is configured to acquire meteorological data in a preset area for storage, determine whether to perform preheating control according to the meteorological data, and when it is determined to perform preheating control, determine a cooling center point according to the meteorological data, and determine a neighborhood boundary range according to the meteorological data of the cooling center point; The processing unit is configured to obtain forecast data of each point within the neighborhood boundary based on the minute-level grid point temperature drop forecast and the CMA-MESO temperature drop forecast; The parsing unit is configured to obtain historical data of each point within the neighborhood boundary range, combine the historical data with the forecast data to obtain a combined data set, construct a Gaussian kernel density function according to the combined data set, draw a peak density map according to the Gaussian kernel density function, and determine the cooling alarm information of the cooling center point according to the peak density map, wherein the cooling alarm information includes a cooling time and a warning level; The adjustment unit is configured to determine the heat storage according to the warning level, collect the real-time temperature change rate in the preset area before the temperature drop moment, and determine whether to adjust the heat storage according to the temperature change rate.

[0017] It is understandable that the above-mentioned energy-saving control method and system for heat pumps have the same beneficial effects, which will not be described in detail here. BRIEF DESCRIPTION OF THE DRAWINGS

[0018] Various other advantages and benefits will become apparent to those of ordinary skill in the art by reading the detailed description of the preferred embodiments below. The accompanying drawings are only for the purpose of illustrating the preferred embodiments and are not to be considered as limiting the present invention. Moreover, the same reference symbols are used throughout the accompanying drawings to represent the same components. In the accompanying drawings: Figure 1 A flow chart of an energy-saving control method for a heat pump provided in an embodiment of the present invention; Figure 2 A structural block diagram of an energy-saving control system for a heat pump provided in an embodiment of the present invention. DETAILED DESCRIPTION

[0019] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying drawings. Although exemplary embodiments of the present disclosure are shown in the accompanying drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments described herein. On the contrary, these embodiments are provided in order to enable a more thorough understanding of the present disclosure and to fully convey the scope of the present disclosure to those skilled in the art. It should be noted that, in the absence of conflict, the embodiments of the present invention and the features described in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the accompanying drawings and in combination with the embodiments.

[0020] In some embodiments of the present application, see Figure 1 As shown, an energy-saving control method for a heat pump comprises: S100: Collecting meteorological data in a preset area for storage, determining whether to perform preheating control based on the meteorological data, and when it is determined to perform preheating control, determining a cooling center point based on the meteorological data, and determining a neighborhood boundary range based on the meteorological data of the cooling center point.

[0021] S200: Obtain forecast data for each point within the neighborhood boundary based on the minute-level grid temperature drop forecast and the CMA-MESO temperature drop forecast.

[0022] S300: Obtain historical data of each point within the neighborhood boundary, combine the historical data with the forecast data to obtain a combined data set, construct a Gaussian kernel density function based on the combined data set, draw a peak density map based on the Gaussian kernel density function, and determine the cooling alarm information of the cooling center point based on the peak density map, the cooling alarm information includes the cooling time and the warning level.

[0023] S400: Determine the heat storage amount according to the warning level, collect the real-time temperature change rate in the preset area before the temperature drop time, and determine whether to adjust the heat storage amount according to the temperature change rate.

[0024] Specifically, S100 collects and stores meteorological data in the preset area, including temperature, humidity, wind speed and other information. The analysis of these data provides a basis for the subsequent judgment of whether preheating control is needed. If the meteorological data determines that there is a cooling trend, the preheating control program is triggered to ensure that the heat pump can prepare for the upcoming low temperature in advance. By monitoring real-time meteorological data and combining the predictive ability of meteorological models, the cooling trend can be identified early, and preparations can be made for the early start of the heat pump to avoid high energy consumption when the temperature changes suddenly. After confirming the cooling trend, S200 obtains the cooling data of each point within the neighborhood boundary based on the minute-level grid cooling forecast and the CMA-MESO (China Meteorological Administration Regional Numerical Forecast) cooling forecast. These forecast data provide detailed temperature change trends in the future period of time, helping to further accurately analyze the cooling mode. By using high-frequency meteorological forecast data, the time, amplitude and distribution of cooling can be accurately predicted, providing data support for the heat pump system to adjust the operation strategy. In S300, historical meteorological data of each point within the neighborhood boundary is collected and combined with the above-mentioned forecast data to form a complete data set. Using these combined data, the density distribution of cooling is modeled by constructing a Gaussian kernel density function, thereby drawing a peak density map. It helps to identify the high probability moment of cooling, and judge the cooling alarm information, including the cooling moment and the warning level, based on the density map. The Gaussian kernel density function is a non-parametric estimation method that can smoothly estimate the probability density of data. It is used to smooth the combination of historical data and forecast data, accurately predict the timing and degree of cooling, and thus achieve more accurate warnings. In S400, the heat storage amount is determined in advance according to the warning level in the cooling alarm information, and the real-time temperature change rate in the preset area is monitored. If the temperature change rate exceeds the predetermined range, the heat storage amount is dynamically adjusted according to the change to ensure the balance between the efficiency of the heat pump system and the temperature control requirements. Temperature change rate monitoring refers to real-time tracking of the rate of change of the current ambient temperature. When a drastic temperature change is detected, the heat storage amount is adjusted in time to ensure that the heat pump maintains the best working state when responding to cooling.

[0025] It is understandable that by combining real-time meteorological data with high-precision temperature drop forecasts and combining historical data analysis, the temperature drop trend can be predicted in advance and the heat storage and operation mode of the heat pump can be adjusted intelligently. By analyzing the temperature drop density and drawing the peak density map using the Gaussian kernel density function, the temperature drop time and intensity can be accurately predicted before the temperature drop occurs, thereby achieving precise preheating control. In addition, real-time temperature change rate monitoring and dynamic adjustment of heat storage allow the heat pump system to always maintain the best energy efficiency under different meteorological conditions. The system's response speed and prediction accuracy are improved, energy waste is reduced, and the operating efficiency and comfort of the heat pump are improved.

[0026] In some embodiments of the present application, when judging whether to perform preheating control based on meteorological data, it includes: comparing the meteorological data of each point in the preset area with the data threshold respectively, and judging whether to perform preheating control based on the comparison result. The meteorological data includes temperature data, and the data threshold includes a temperature threshold.

[0027] Specifically, when the temperature data is less than the temperature threshold, it is determined that the warm-up control is to be performed, and when the temperature data is greater than or equal to the temperature threshold, it is determined that the warm-up control is not to be performed.

[0028] It is understandable that when the ambient temperature is lower than the set threshold, the heat pump system can start preheating in time, avoiding the sudden impact of low temperature and ensuring the comfort of the indoor environment. When the temperature is sufficient, unnecessary preheating control is automatically avoided, saving energy and reducing the burden on the heat pump system. The control process is simplified, the flexibility of response is improved, and energy waste is effectively reduced.

[0029] In some embodiments of the present application, when determining the neighborhood boundary range according to the meteorological data of the cooling center point, it includes: The point in the preset area where the meteorological data is greater than the data threshold is selected as the cooling center point, and the neighborhood boundary range is determined according to the real-time meteorological data of the cooling center point. The neighborhood boundary range is inversely proportional to the real-time meteorological data.

[0030] It is understandable that when the meteorological data of the cooling center point changes, the size of the focus area can be flexibly adjusted according to the actual meteorological conditions, thereby improving the accuracy of preheating control and avoiding over-preheating or missing important areas. It achieves a higher precision prediction and control of the cooling trend, effectively improves the energy efficiency of the heat pump, reduces energy waste, and ensures timely response to temperature control needs.

[0031] In some embodiments of the present application, when obtaining forecast data of each point within the neighborhood boundary based on the minute-level grid temperature drop forecast and the CMA-MESO temperature drop forecast, it includes: Based on the minute-level grid temperature drop forecast, the forecast data for the neighborhood boundary range of 0-2h is obtained.

[0032] Based on the CMA-MESO temperature drop forecast, the forecast data for the neighborhood boundary range of 2-12h is obtained.

[0033] The forecast data of the neighborhood boundary range of 2-12h is converted from resolution Convert to .

[0034] The transformed forecast data of the neighborhood boundary range of 2-12 hours are fused with the forecast data of the neighborhood boundary range of 0-2 hours to obtain the forecast data of the neighborhood boundary range of 0-12 hours.

[0035] In some embodiments of the present application, combining historical data with forecast data to obtain a combined data set includes: Obtain the meteorological data of the neighborhood boundary within the past 3 hours based on historical data.

[0036] The forecast data for the next 3 hours is obtained based on the forecast data of the neighborhood boundary range of 0-12 hours.

[0037] Combine the meteorological data of the past 3 hours with the forecast data of the next 3 hours to obtain a combined data set.

[0038] In some embodiments of the present application, when constructing a Gaussian kernel density function based on the combined data set, the Gaussian kernel density function is: ; Where n represents the number of data samples in the combined data set, h represents the smoothing bandwidth, and s i represents the i-th data point in the combined data set, and x represents any variable in the overall data.

[0039] The smoothing bandwidth is calculated by the following formula: ; Among them, h represents the smoothing bandwidth, σ represents the standard deviation of the data in the combined data set, β1 represents the data skewness, β2 represents the data kurtosis, n represents the number of data samples in the combined data set, and k represents the adjustment coefficient.

[0040] The data skewness is calculated by the following formula: ; The kurtosis of the data is calculated by the following formula: ; Among them, n represents the number of data samples in the combined data set, s i represents the i-th data point in the combined data set, represents the mean of the data in the combined data set, and σ represents the standard deviation of the data in the combined data set.

[0041] In some embodiments of the present application, when determining the cooling alarm information of the cooling center point according to the peak density map, it includes: The meteorological data difference is obtained according to the meteorological data corresponding to the highest value of the density estimation value in the peak density map and the data threshold, the temperature drop time is obtained according to the meteorological data corresponding to the highest value of the density estimation value in the peak density map and the forecast data, the meteorological data difference is compared with the first preset difference and the second preset difference respectively, and the warning level of the temperature drop center point is determined according to the comparison result. The first preset difference is smaller than the second preset difference.

[0042] When the difference in meteorological data is less than or equal to the first preset difference, the first cooling level is determined to be the warning level. When the difference in meteorological data is greater than the first preset difference and less than or equal to the second preset difference, the second cooling level is determined to be the warning level. When the difference in meteorological data is greater than the second preset difference, the third cooling level is determined to be the warning level. The first cooling level indicates that the degree of cooling is higher than the second cooling level, and the second cooling level indicates that the degree of cooling is higher than the third cooling level.

[0043] It is understandable that through high-precision meteorological data fusion and density estimation of Gaussian kernel density function, it is possible to more accurately predict the cooling trend and respond to cooling events of different intensities in a timely manner. Through the combination of minute-level grid forecasts and CMA-MESO forecasts, cooling data of different time scales are obtained, which can provide accurate warnings over a longer time range. Based on the combination of historical data and forecast data, it is possible to judge the changes in the cooling center within a wider time interval, providing a more flexible and accurate cooling control strategy. Through multi-level warning of cooling alarm information, reasonable heat storage adjustments and preheating controls can be made before cooling occurs, thereby effectively improving the energy efficiency of the heat pump and reducing energy waste.

[0044] In some embodiments of the present application, the real-time temperature change rate in a preset area before the temperature drop moment is collected, and whether the heat storage is to be adjusted according to the temperature change rate is determined, including: obtaining a predicted temperature change rate according to the meteorological data corresponding to the highest value of the density estimation value in the peak density map and the temperature drop moment, comparing the temperature change rate with the predicted temperature change rate, and determining whether the heat storage is to be adjusted according to the comparison result.

[0045] In some embodiments of the present application, judging whether to adjust the heat storage amount according to the comparison result includes: When the temperature change rate is less than the predicted temperature change rate, it is determined that the heat storage is adjusted, and the adjustment coefficient is determined according to the absolute value of the difference between the temperature change rate and the predicted temperature change rate to adjust the heat storage, and the adjustment coefficient is proportional to the absolute value of the difference. When the temperature change rate is greater than or equal to the predicted temperature change rate, it is determined that the heat storage is not adjusted.

[0046] Specifically, when it is determined that the heat storage is to be adjusted, the adjusted heat storage is the product of the current heat storage and the adjustment coefficient, and the adjustment coefficient is (1, 1.5]. When the temperature change rate is less than the predicted temperature change rate, it is determined that the heat storage needs to be adjusted. According to the absolute value of the difference between the temperature change rate and the predicted rate, the adjustment coefficient is calculated, and the heat storage is adjusted proportionally according to the adjustment coefficient. The larger the temperature change rate difference, the greater the heat storage adjustment.

[0047] It is understandable that by accurately comparing the real-time temperature change rate with the predicted temperature change rate, the heat storage can be flexibly adjusted to cope with the changing cooling trend. The heat storage can be actively increased when the cooling rate is faster, improving the heat pump response capability, avoiding the waste of heat storage resources when the cooling rate is slower, and improving energy efficiency. By calculating the adjustment coefficient of the difference between the temperature change rate and the predicted rate, the heat storage can be dynamically optimized according to the actual environmental changes, which saves energy and improves the stability of the heat pump system.

[0048] In the above embodiments, the energy saving and response efficiency of the heat pump system are improved through prediction and intelligent adjustment. By collecting and storing meteorological data, and combining minute-level temperature drop forecasts and historical data, the temperature drop trend can be accurately judged and preheating control can be performed in advance. Dynamic adjustment based on meteorological data avoids the traditional method of relying on simple temperature thresholds, and can make accurate responses under complex meteorological conditions. The peak density map is constructed by the Gaussian kernel density function to further optimize the temperature drop alarm information, ensuring that the heat pump adjusts the operation strategy in time before the temperature drop moment to avoid unnecessary energy waste. At the same time, combined with the real-time temperature change rate monitoring, the heat storage capacity is finely adjusted so that the heat pump operates at the optimal energy efficiency state. The heat pump's ability to adapt to sudden weather changes is improved, energy consumption is reduced, and the comfort of indoor temperature control is ensured.

[0049] In another preferred embodiment based on the above embodiment, refer to Figure 2 As shown, this embodiment provides an energy-saving control system for a heat pump, which is used to apply the above energy-saving control method for a heat pump, including: The heat storage module is configured to store high-temperature medium.

[0050] The control module includes a collection unit, a processing unit, a parsing unit and an adjustment unit, wherein: The collection unit is configured to collect meteorological data within a preset area for storage, determine whether to perform preheating control based on the meteorological data, and when it is determined to perform preheating control, determine the cooling center point based on the meteorological data, and determine the neighborhood boundary range based on the meteorological data of the cooling center point.

[0051] The processing unit is configured to obtain forecast data of each point within the neighborhood boundary based on the minute-level grid point temperature drop forecast and the CMA-MESO temperature drop forecast.

[0052] The parsing unit is configured to obtain historical data of each point within the neighborhood boundary, combine the historical data with the forecast data to obtain a combined data set, construct a Gaussian kernel density function based on the combined data set, draw a peak density map based on the Gaussian kernel density function, and determine the cooling alarm information of the cooling center point based on the peak density map, and the cooling alarm information includes the cooling time and the warning level.

[0053] The adjustment unit is configured to determine the heat storage amount according to the warning level, collect the real-time temperature change rate in the preset area before the temperature drop moment, and determine whether to adjust the heat storage amount according to the temperature change rate.

[0054] It is understandable that the energy saving and response efficiency of the heat pump system have been improved through prediction and intelligent adjustment. By collecting and storing meteorological data, combined with minute-level cooling forecasts and historical data, it is possible to accurately judge the cooling trend and perform preheating control in advance. Dynamic adjustment based on meteorological data avoids the traditional method of relying on simple temperature thresholds and can respond accurately under complex meteorological conditions. The peak density map is constructed by the Gaussian kernel density function to further optimize the cooling alarm information, ensuring that the heat pump adjusts its operation strategy in time before the cooling moment to avoid unnecessary energy waste. At the same time, combined with the real-time temperature change rate monitoring, the heat storage capacity is finely adjusted so that the heat pump operates at the optimal energy efficiency state. The heat pump's ability to adapt to sudden weather changes is improved, energy consumption is reduced, and the comfort of indoor temperature control is ensured.

[0055] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems or computer program products. Therefore, the present application may take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware. Moreover, the present application may take the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) containing computer-usable program codes.

[0056] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems) and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0057] These computer program instructions may also be stored in a computer readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture including an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0058] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0059] 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. Although the present invention has been described in detail with reference to the above embodiments, ordinary technicians in the relevant field should understand that the specific implementation methods of the present invention can still be modified or replaced by equivalents. Any modification or equivalent replacement that does not depart from the spirit and scope of the present invention should be covered within the scope of protection of the claims of the present invention.

Claims

1. An energy-saving control method for a heat pump, characterized in that: include: Collecting meteorological data in a preset area for storage, determining whether to perform preheating control based on the meteorological data, and when it is determined that the preheating control is to be performed, determining a cooling center point based on the meteorological data, and determining a neighborhood boundary range based on the meteorological data of the cooling center point; Obtain forecast data for each point within the neighborhood boundary based on the minute-level grid temperature drop forecast and the CMA-MESO temperature drop forecast; Obtaining historical data of each point within the neighborhood boundary, combining the historical data with forecast data to obtain a combined data set, constructing a Gaussian kernel density function according to the combined data set, drawing a peak density map according to the Gaussian kernel density function, and determining cooling alarm information of the cooling center point according to the peak density map, wherein the cooling alarm information includes a cooling time and a warning level; The heat storage amount is determined according to the warning level, the real-time temperature change rate in the preset area before the temperature drop moment is collected, and whether to adjust the heat storage amount is determined according to the temperature change rate.

2. The energy-saving control method for a heat pump according to claim 1, characterized in that: The step of determining whether to perform preheating control according to the meteorological data includes: Comparing the meteorological data of each point in the preset area with the data threshold respectively, and judging whether to perform preheating control according to the comparison result; the meteorological data includes temperature data, and the data threshold includes a temperature threshold; When the temperature data is less than the temperature threshold, determining to perform the preheating control; When the temperature data is greater than or equal to a temperature threshold, it is determined that the warm-up control is not performed.

3. The energy-saving control method for a heat pump according to claim 2, characterized in that: When determining the neighborhood boundary range based on the meteorological data of the cooling center point, it includes: A point in the preset area where the meteorological data is greater than the data threshold is selected as the cooling center point, and the neighborhood boundary range is determined according to the real-time meteorological data of the cooling center point. The neighborhood boundary range is inversely proportional to the real-time meteorological data.

4. The energy-saving control method for a heat pump according to claim 3, characterized in that: When obtaining forecast data for each point within the neighborhood boundary based on the minute-level grid temperature drop forecast and the CMA-MESO temperature drop forecast, it includes: Obtain forecast data for the neighborhood boundary range of 0-2 hours based on the minute-level grid temperature drop forecast; Obtain forecast data for the neighborhood boundary range of 2-12 hours based on the CMA-MESO temperature drop forecast; The forecast data of the neighborhood boundary range of 2-12h is converted from resolution Convert to ; The transformed forecast data for the neighborhood boundary range of 2-12 hours are fused with the forecast data for the neighborhood boundary range of 0-2 hours to obtain the forecast data for the neighborhood boundary range of 0-12 hours.

5. The energy-saving control method for a heat pump according to claim 4, characterized in that: Combining the historical data with the forecast data to obtain a combined data set includes: Obtain the meteorological data of the neighborhood boundary range in the past 3 hours according to the historical data; Obtaining forecast data for the next 3 hours based on the forecast data of the neighborhood boundary range of 0-12 hours; The meteorological data for the past 3 hours are combined with the forecast data for the next 3 hours to obtain the combined data set.

6. The energy-saving control method for a heat pump according to claim 5, characterized in that: When constructing a Gaussian kernel density function according to the combined data set, the Gaussian kernel density function is: ； Where n represents the number of data samples in the combined data set, h represents the smoothing bandwidth, represents the i-th data point in the combined data set, and x represents any variable in the overall data; The smoothing bandwidth is calculated by the following formula: ； Among them, h represents the smoothing bandwidth, σ represents the standard deviation of the data in the combined data set, β1 represents the data skewness, β2 represents the data kurtosis, n represents the number of data samples in the combined data set, and k represents the adjustment coefficient; The data skewness is calculated by the following formula: ； The data kurtosis is calculated by the following formula: ; Where n represents the number of data samples in the combined data set, represents the i-th data point in the combined data set, represents the mean of the data in the combined data set, and σ represents the standard deviation of the data in the combined data set.

7. The energy-saving control method for a heat pump according to claim 6, characterized in that: When determining the cooling alarm information of the cooling center point according to the peak density map, it includes: Obtain a meteorological data difference according to the meteorological data corresponding to the highest value of the density estimation value in the peak density map and the data threshold, obtain the cooling time according to the meteorological data corresponding to the highest value of the density estimation value in the peak density map and the forecast data, compare the meteorological data difference with the first preset difference and the second preset difference respectively, and determine the warning level of the cooling center point according to the comparison result; the first preset difference is less than the second preset difference; When the meteorological data difference is less than or equal to a first preset difference, the first cooling level is determined as the warning level; when the meteorological data difference is greater than the first preset difference and less than or equal to a second preset difference, the second cooling level is determined as the warning level; when the meteorological data difference is greater than the second preset difference, the third cooling level is determined as the warning level; the first cooling level indicates that the degree of cooling is higher than the second cooling level, and the second cooling level indicates that the degree of cooling is higher than the third cooling level.

8. The energy-saving control method for a heat pump according to claim 7, characterized in that: Collecting the real-time temperature change rate in the preset area before the temperature drop time, and judging whether to adjust the heat storage amount according to the temperature change rate, includes: The predicted temperature change rate is obtained based on the meteorological data corresponding to the highest value of the density estimation value in the peak density map and the temperature drop time, the temperature change rate is compared with the predicted temperature change rate, and it is determined whether to adjust the heat storage according to the comparison result.

9. The energy-saving control method for a heat pump according to claim 8, characterized in that: When judging whether to adjust the heat storage amount according to the comparison result, it includes: When the temperature change rate is less than the predicted temperature change rate, it is determined that the heat storage is adjusted, and an adjustment coefficient is determined according to the absolute value of the difference between the temperature change rate and the predicted temperature change rate to adjust the heat storage, and the adjustment coefficient is proportional to the absolute value of the difference; when the temperature change rate is greater than or equal to the predicted temperature change rate, it is determined that the heat storage is not adjusted.

10. An energy-saving control system for a heat pump, used for applying the energy-saving control method for a heat pump according to any one of claims 1 to 9, characterized in that: include: A heat storage module configured to store a high temperature medium; The control module includes a collection unit, a processing unit, a parsing unit and an adjustment unit, wherein: The acquisition unit is configured to acquire meteorological data in a preset area for storage, determine whether to perform preheating control according to the meteorological data, and when it is determined to perform preheating control, determine a cooling center point according to the meteorological data, and determine a neighborhood boundary range according to the meteorological data of the cooling center point; The processing unit is configured to obtain forecast data of each point within the neighborhood boundary based on the minute-level grid point temperature drop forecast and the CMA-MESO temperature drop forecast; The parsing unit is configured to obtain historical data of each point within the neighborhood boundary range, combine the historical data with the forecast data to obtain a combined data set, construct a Gaussian kernel density function according to the combined data set, draw a peak density map according to the Gaussian kernel density function, and determine the cooling alarm information of the cooling center point according to the peak density map, wherein the cooling alarm information includes a cooling time and a warning level; The adjustment unit is configured to determine the heat storage according to the warning level, collect the real-time temperature change rate in the preset area before the temperature drop moment, and determine whether to adjust the heat storage according to the temperature change rate.

Images