An energy-saving control method and system for a heat pump

Abstract

The present invention relates to the technical field of heating management, and discloses an energy-saving control method and system for a heat pump. The method includes: collecting meteorological data in a preset area to determine whether to perform preheating control. When it is determined to perform preheating control, determining a cooling center point and determining a neighborhood boundary range; obtaining forecast data of each point within the neighborhood boundary range; obtaining historical data of each point within the neighborhood boundary range, combining it with the forecast data to obtain a combined data set, constructing a Gaussian kernel density function to determine the cooling warning information of the cooling center point; determining the heat storage amount according to the warning level, and collecting the real-time temperature change rate before the cooling moment in the preset area to determine whether to adjust the heat storage amount. This application improves the energy-saving and response efficiency of the heat pump system through prediction and intelligent adjustment.

Description

Technical Field

[0001] The present invention relates to the technical field of heating 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 conservation 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 the low-temperature heat in the external environment to provide comfortable temperature control services for buildings.

[0003] However, most current heat pump control technologies are based on environmental temperature sensors and traditional constant temperature regulation methods. When facing complex meteorological condition changes, these methods cannot make predictions and effective adjustments. For example, when sudden temperature drops or extreme weather occur, the heat pump adjusts the valve by detecting the current temperature to ensure that the indoor temperature is maintained within a comfortable range. Relying on simple temperature thresholds or empirical rules, the prediction characteristics of meteorological data are not fully considered, resulting in the system being unable to predict and respond to meteorological changes in a timely manner. Moreover, simply increasing the heat pump operating efficiency to maintain the indoor temperature in case of temperature drops 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 problems of lack of predictive regulation, resulting in adjustment lag and energy waste, when heating is performed based on a heat pump.

[0006] On the one hand, the present invention proposes an energy-saving control method for a heat pump, including:

[0007] Collect meteorological data within a preset area for storage, and determine whether to perform preheating control according to the meteorological data. When it is determined to perform the preheating control, determine the cooling center point according to the meteorological data, and determine the neighborhood boundary range according to the meteorological data of the cooling center point;

[0008] Obtain the forecast data of each point within the neighborhood boundary range based on the minute-level grid temperature drop forecast and the CMA-MESO temperature drop forecast;

[0009] Obtain the 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 warning information of the cooling center point according to the peak density map. The cooling warning information includes the cooling time and the warning level;

[0010] Determine the heat storage amount according to the warning level, collect the real-time temperature change rate in the preset area before the cooling moment, and judge whether to adjust the heat storage amount according to the temperature change rate.

[0011] Further, when judging whether to perform preheating control according to the meteorological data, it includes:

[0012] Compare the meteorological data of each point in the preset area with the data threshold respectively, and judge whether to perform preheating control according to the comparison result; the meteorological data includes temperature data, and the data threshold includes temperature threshold;

[0013] When the temperature data is less than the temperature threshold, it is determined to perform the preheating control;

[0014] When the temperature data is greater than or equal to the temperature threshold, it is determined not to perform the preheating control.

[0015] Further, when determining the neighborhood boundary range according to the meteorological data of the cooling center point, it includes:

[0016] Select the points in the preset area where the meteorological data is greater than the data threshold as the cooling center point, and determine the neighborhood boundary range 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.

[0017] Further, when obtaining the forecast data of each point in the neighborhood boundary range based on the minute-level grid cooling forecast and the CMA-MESO cooling forecast, it includes:

[0018] Obtain the forecast data of the neighborhood boundary range from 0 to 2h based on the minute-level grid cooling forecast;

[0019] Obtain the forecast data of the neighborhood boundary range from 2 to 12h based on the CMA-MESO cooling forecast;

[0020] Use the nearest neighbor interpolation method to convert the forecast data of the neighborhood boundary range from 2 to 12h from the resolution to ;

[0021] Fuse the converted forecast data of the neighborhood boundary range from 2 to 12h with the forecast data of the neighborhood boundary range from 0 to 2h to obtain the forecast data of the neighborhood boundary range from 0 to 12h.

[0022] Further, when combining the historical data and the forecast data to obtain a combined data set, it includes:

[0023] Obtain the meteorological data of the neighborhood boundary range in the past 3h according to the historical data;

[0024] Obtain the forecast data for the next 3h based on the forecast data within the neighborhood boundary range of 0 - 12h;

[0025] Combine the meteorological data for the past 3h with the forecast data for the next 3h to obtain the combined data set.

[0026] Further, when constructing the Gaussian kernel density function based on the combined data set, the Gaussian kernel density function is:

[0027] ;

[0028] where n represents the number of data samples in the combined data set, h represents the smoothing bandwidth, s i represents the i-th data point in the combined data set, and x represents any variable in the overall data;

[0029] where the smoothing bandwidth is calculated by the following formula:

[0030] ;

[0031] where h represents the smoothing bandwidth, σ represents the standard deviation of the data in the combined data set, β1 represents the skewness of the data, β2 represents the kurtosis of the data, n represents the number of data samples in the combined data set, and k represents the adjustment coefficient;

[0032] where the skewness of the data is calculated by the following formula:

[0033] ;

[0034] The kurtosis of the data is calculated by the following formula:

[0035] ;

[0036] where 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.

[0037] Further, when determining the cooling warning information for the cooling center point based on the peak density map, it includes:

[0038] Obtain the meteorological data difference based on 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 moment based on 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;

[0039] When the meteorological data difference is less than or equal to the first preset difference, determine that the first cooling level is the warning level; when the meteorological data difference is greater than the first preset difference and less than or equal to the second preset difference, determine that the second cooling level is the warning level; when the meteorological data difference is greater than the second preset difference, determine that the third cooling level is the warning level; the first cooling level indicates that the cooling degree is higher than the second cooling level, and the second cooling level indicates that the cooling degree is higher than the third cooling level.

[0040] Further, when collecting the real-time temperature change rate before the cooling moment in the preset area and judging whether to adjust the heat storage amount according to the temperature change rate, it includes:

[0041] Obtain the predicted temperature change rate based on the meteorological data corresponding to the highest value of the density estimation value in the peak density map and the cooling moment, compare the temperature change rate with the predicted temperature change rate, and judge whether to adjust the heat storage amount according to the comparison result.

[0042] Further, when judging whether to adjust the heat storage amount according to the comparison result, it includes:

[0043] When the temperature change rate is less than the predicted temperature change rate, it is determined to adjust the heat storage amount, 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 amount, and the adjustment coefficient is in a direct proportional relationship with 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 not to adjust the heat storage amount.

[0044] Compared with the prior art, the beneficial effects of the present invention are as follows: 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 cooling forecasts and historical data, the cooling trend can be accurately judged and preheating control can be carried out in advance. Based on the dynamic adjustment of meteorological data, the traditional method that relies on simple temperature thresholds is avoided, and accurate responses can be made under complex meteorological conditions. By constructing a peak density map through the Gaussian kernel density function, the cooling warning information is further optimized to ensure that the heat pump adjusts its operation strategy in a timely manner before the cooling moment, avoiding unnecessary energy waste. At the same time, by combining the monitoring of the real-time temperature change rate, the heat storage amount is finely adjusted, enabling the heat pump to operate at the optimal energy efficiency state. The adaptability of the heat pump to sudden weather changes is improved, energy consumption is reduced, and the comfort of indoor temperature control is ensured.

[0045] 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-mentioned energy-saving control method for a heat pump, and includes:

[0046] A heat storage module configured to store a high-temperature medium;

[0047] A control module including a collection unit, a processing unit, an analysis unit, and an adjustment unit, wherein,

[0048] The collection unit is configured to collect and store meteorological data in a preset area, judge whether to perform preheating control according to the meteorological data, when it is determined to perform the 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;

[0049] The processing unit is configured to obtain forecast data of each point within the neighborhood boundary range based on the minute-level grid cooling forecast and the CMA-MESO cooling forecast;

[0050] The analysis unit is configured to obtain the 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 warning information of the cooling center point according to the peak density map, where the cooling warning information includes the cooling moment and the warning level;

[0051] 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 cooling moment, and judge whether to adjust the heat storage amount according to the temperature change rate.

[0052] It can be understood that the above-mentioned energy-saving control method and system for a heat pump have the same beneficial effects, which will not be elaborated here. Description of the Drawings

[0053] By reading the following detailed description of the preferred embodiments, various other advantages and benefits will become clear to those of ordinary skill in the art. The drawings are only for the purpose of showing the preferred embodiments and are not considered to be a limitation of the present invention. Moreover, throughout the drawings, the same reference numerals are used to represent the same components. In the drawings:

[0054] Figure 1 is a flowchart of an energy-saving control method for a heat pump provided by an embodiment of the present invention;

[0055] Figure 2 is a structural block diagram of an energy-saving control system for a heat pump provided by an embodiment of the present invention. Specific Embodiments

[0056] The exemplary embodiments of the present disclosure will be described in more detail below with reference to the drawings. Although the exemplary embodiments of the present disclosure are shown in the drawings, it should be understood that the present disclosure can be implemented in various forms and should not be limited by the embodiments set forth herein. On the contrary, these embodiments are provided so that the present disclosure can be more thoroughly understood and the scope of the present disclosure can be fully conveyed to those skilled in the art. It should be noted that, without conflict, the embodiments in the present invention and the features in the embodiments can be combined with each other. The present invention will be described in detail below with reference to the drawings and in conjunction with the embodiments.

[0057] In some embodiments of the present application, referring to Figure 1 as shown, an energy-saving control method for a heat pump includes:

[0058] S100: Collect meteorological data in a preset area for storage, determine whether to perform preheating control according to the meteorological data. When it is determined to perform preheating control, determine the cooling center point according to the meteorological data, and determine the neighborhood boundary range according to the meteorological data of the cooling center point.

[0059] S200: Obtain the forecast data of each point within the neighborhood boundary range based on the minute-level grid cooling forecast and the CMA-MESO cooling forecast.

[0060] S300: Obtain the 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 warning information of the cooling center point according to the peak density map. The cooling warning information includes the cooling time and the warning level.

[0061] S400: Determine the heat storage amount according to the warning level, collect the real-time temperature change rate in the preset area before the cooling time, and determine whether to adjust the heat storage amount according to the temperature change rate.

[0062] Specifically, in S100, meteorological data within a preset area are collected, including information such as temperature, humidity, wind speed, etc., and stored. The analysis of these data provides a basis for subsequent judgment on whether preheating control is required. If a cooling trend is judged based on the meteorological data, the preheating control program is triggered to ensure that the heat pump can be prepared in advance to cope with the upcoming low temperature. By monitoring the real-time meteorological data and combining with the prediction ability of the meteorological model, the cooling trend can be identified in advance, preparing for the heat pump to start preheating ahead of time and avoiding high-energy consumption startup when the temperature suddenly changes. In S200, after confirming the cooling trend, according to the minute-level grid cooling forecast and the CMA-MESO (China Meteorological Administration Regional Numerical Forecast) cooling forecast, the cooling data of each point within the neighborhood boundary range are obtained. These forecast data provide the detailed temperature change trend within a certain period in the future, helping to further accurately analyze the cooling mode. By using high-frequency meteorological forecast data, the time, amplitude, and distribution of the cooling are accurately predicted, providing data support for the heat pump system to adjust the operation strategy. In S300, the historical meteorological data of each point within the neighborhood boundary range are collected and combined with the above-mentioned forecast data to form a complete data set. Using these combined data, by constructing a Gaussian kernel density function, the density distribution of the cooling is modeled, and thus the peak density map is drawn. This helps to identify the high-probability moments of the cooling and judge the cooling warning information according to the density map, including the cooling moment and the warning level. The Gaussian kernel density function is a non-parametric estimation method that can smoothly estimate the probability density of the data. It is used to smooth the combined result of the historical data and the forecast data, accurately predicting the timing and degree of the cooling, so as to achieve more accurate warning. In S400, according to the warning level in the cooling warning information, the heat storage capacity is determined in advance, and the real-time temperature change rate within the preset area is monitored. If the temperature change rate exceeds the predetermined range, the heat storage capacity is dynamically adjusted according to the change situation to ensure the balance between the efficiency of the heat pump system and the temperature control requirements. The monitoring of the temperature change rate refers to the real-time tracking of the rate of change of the current ambient temperature. When a drastic temperature change is detected, the heat storage capacity is adjusted in a timely manner to ensure that the heat pump maintains the best working state when coping with the cooling.

[0063] It can be understood that by combining real-time meteorological data with high-precision cooling forecasts and combining with historical data analysis, the cooling trend is predicted in advance and the heat storage capacity and operation mode of the heat pump are intelligently adjusted. Through the analysis of the cooling density by the Gaussian kernel density function and the drawing of the peak density map, the cooling moment and intensity are accurately predicted before the cooling occurs, thus realizing precise preheating control. In addition, the monitoring of the real-time temperature change rate and the dynamic adjustment of the heat storage capacity enable the heat pump system to always maintain the best energy efficiency state under different meteorological conditions. This improves the response speed and prediction accuracy of the system, reduces energy waste, and enhances the operation efficiency and comfort of the heat pump.

[0064] In some embodiments of the present application, when determining whether to perform preheating control based on meteorological data, it includes: comparing the meteorological data of each point within a preset area with a data threshold respectively, and determining whether to perform preheating control according to the comparison result. The meteorological data includes temperature data, and the data threshold includes a temperature threshold.

[0065] Specifically, when the temperature data is less than the temperature threshold, it is determined to perform preheating control. When the temperature data is greater than or equal to the temperature threshold, it is determined not to perform preheating control.

[0066] It can be understood 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 response flexibility is improved, and energy waste is effectively reduced.

[0067] 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:

[0068] Selecting the points corresponding to the meteorological data greater than the data threshold within the preset area as the cooling center points, and determining the neighborhood boundary range according to the real-time meteorological data of the cooling center points. The neighborhood boundary range is inversely proportional to the real-time meteorological data.

[0069] It can be understood that when the meteorological data of the cooling center point changes, the size of the concerned area is flexibly adjusted according to the actual meteorological conditions, thereby improving the accuracy of preheating control, avoiding over-preheating or missing important areas. Higher-precision prediction and control of the cooling trend are realized, the energy efficiency of the heat pump is effectively improved, energy waste is reduced, and the timely response to temperature control requirements is ensured.

[0070] In some embodiments of the present application, when obtaining the forecast data of each point within the neighborhood boundary range based on the minute-level grid cooling forecast and the CMA-MESO cooling forecast, it includes:

[0071] Obtaining the forecast data of 0-2h within the neighborhood boundary range based on the minute-level grid cooling forecast.

[0072] Obtaining the forecast data of 2-12h within the neighborhood boundary range based on the CMA-MESO cooling forecast.

[0073] Using the nearest neighbor interpolation method to convert the forecast data of 2-12h within the neighborhood boundary range from the resolution to .

[0074] Fusing the converted forecast data of 2-12h within the neighborhood boundary range with the forecast data of 0-2h within the neighborhood boundary range to obtain the forecast data of 0-12h within the neighborhood boundary range.

[0075] In some embodiments of the present application, when combining historical data and forecast data to obtain a combined data set, it includes:

[0076] Obtain meteorological data for the past 3 hours within the neighborhood boundary range based on the historical data.

[0077] Obtain the forecast data for the next 3 hours based on the forecast data for 0 - 12 hours within the neighborhood boundary range.

[0078] Combine the meteorological data for the past 3 hours and the forecast data for the next 3 hours to obtain a combined data set.

[0079] 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:

[0080] ;

[0081] where n represents the number of data samples in the combined data set, h represents the smoothing bandwidth, s i represents the i-th data point in the combined data set, and x represents any variable in the overall data.

[0082] The smoothing bandwidth is calculated by the following formula:

[0083] ;

[0084] where h represents the smoothing bandwidth, σ represents the standard deviation of the data in the combined data set, β1 represents the skewness of the data, β2 represents the kurtosis of the data, n represents the number of data samples in the combined data set, and k represents the adjustment coefficient.

[0085] The skewness of the data is calculated by the following formula:

[0086] ;

[0087] The kurtosis of the data is calculated by the following formula:

[0088] ;

[0089] where 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.

[0090] In some embodiments of the present application, when determining the cooling warning information of the cooling center point according to the peak density map, it includes:

[0091] Obtain the meteorological data difference based on the meteorological data corresponding to the highest value of the density estimate in the peak density map and the data threshold. Obtain the cooling moment based on the meteorological data corresponding to the highest value of the density estimate 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 according to the comparison result. The first preset difference is less than the second preset difference.

[0092] When the meteorological data difference is less than or equal to the first preset difference, determine that the first cooling level is the warning level. When the meteorological data difference is greater than the first preset difference and less than or equal to the second preset difference, determine that the second cooling level is the warning level. When the meteorological data difference is greater than the second preset difference, determine that the third cooling level is the warning level. The first cooling level indicates a higher cooling degree than the second cooling level, and the second cooling level indicates a higher cooling degree than the third cooling level.

[0093] It can be understood that through high-precision meteorological data fusion and density estimation of the Gaussian kernel density function, the cooling trend can be predicted more accurately, and different intensity cooling events can be responded to in a timely manner. By combining the minute-level grid forecast and the CMA-MESO forecast, cooling data at different time scales are obtained, and accurate early warnings can be given within a longer time range. Based on the combination of historical data and forecast data, the changes of the cooling center can be judged within a wider time interval, providing a more flexible and accurate cooling control strategy. Through the multi-level early warning of the cooling alarm information, reasonable heat storage adjustment and preheating control can be made before the cooling occurs, thus effectively improving the energy efficiency of the heat pump and reducing energy waste.

[0094] In some embodiments of the present application, when collecting the real-time temperature change rate before the cooling moment in a preset area and judging whether to adjust the heat storage according to the temperature change rate, it includes: obtaining the predicted temperature change rate based on the meteorological data corresponding to the highest value of the density estimate in the peak density map and the cooling moment, comparing the temperature change rate with the predicted temperature change rate, and judging whether to adjust the heat storage according to the comparison result.

[0095] In some embodiments of the present application, when judging whether to adjust the heat storage according to the comparison result, it includes:

[0096] 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 in a direct proportional relationship with 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.

[0097] Specifically, when it is determined that the heat storage amount needs to be adjusted, the adjusted heat storage amount is the product of the current heat storage amount and the adjustment coefficient, and the adjustment coefficient ranges from (1, 1.5]. When the temperature change rate is less than the predicted temperature change rate, it is determined that the heat storage amount 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 amount is adjusted proportionally according to the adjustment coefficient. The greater the difference in the temperature change rate, the greater the adjustment range of the heat storage amount.

[0098] It can be understood that by accurately comparing the real-time temperature change rate with the predicted temperature change rate, the heat storage amount can be flexibly adjusted to cope with the changing cooling trend. It can actively increase the heat storage amount when the cooling speed is fast, improve the response ability of the heat pump, avoid wasting heat storage resources when the cooling is slow, and improve the energy efficiency. Through the calculation of the adjustment coefficient based on the difference between the temperature change rate and the predicted rate, the heat storage amount can be dynamically optimized according to the actual environmental changes, saving energy and improving the stability of the heat pump system.

[0099] In the above embodiments, through prediction and intelligent adjustment, the energy-saving and response efficiency of the heat pump system are improved. By collecting and storing meteorological data, combining minute-level cooling forecasts and historical data, the cooling trend can be accurately judged and preheating control can be carried out in advance. Based on the dynamic adjustment of meteorological data, the traditional method relying on simple temperature thresholds is avoided, and accurate responses can be made under complex meteorological conditions. By constructing a peak density map through the Gaussian kernel density function, the cooling warning information is further optimized to ensure that the heat pump adjusts the operation strategy in a timely manner before the cooling moment, avoiding unnecessary energy waste. At the same time, by combining the monitoring of the real-time temperature change rate, the heat storage amount is finely adjusted, enabling the heat pump to operate in the optimal energy efficiency state. It improves the adaptability of the heat pump to sudden weather changes, reduces energy consumption, and ensures the comfort of indoor temperature control.

[0100] In another preferred manner based on the above embodiments, 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:

[0101] A heat storage module configured to store a high-temperature medium.

[0102] A control module includes a collection unit, a processing unit, an analysis unit, and an adjustment unit, where

[0103] The collection unit is configured to collect and store meteorological data in a preset area, determine whether to perform preheating control according to the meteorological data, and when it is determined to perform preheating control, determine the cooling center point according to the meteorological data, and determine the neighborhood boundary range according to the meteorological data of the cooling center point.

[0104] The processing unit is configured to obtain the forecast data of each point within the neighborhood boundary based on the minute-level grid cooling forecast and the CMA-MESO cooling forecast.

[0105] The parsing unit is configured to obtain the 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 according to the combined data set, draw a peak density map according to the Gaussian kernel density function, and determine the cooling warning information of the cooling center point according to the peak density map. The cooling warning information includes the cooling time and the warning level.

[0106] 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 cooling time, and judge whether to adjust the heat storage amount according to the temperature change rate.

[0107] It can be understood that 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 the minute-level cooling forecast and historical data, the cooling trend can be accurately judged and preheating control can be carried out in advance. The dynamic adjustment based on meteorological data avoids the traditional method that relies on simple temperature thresholds and can make accurate responses under complex meteorological conditions. By constructing a peak density map through the Gaussian kernel density function, the cooling warning information is further optimized to ensure that the heat pump adjusts its operation strategy in a timely manner before the cooling time, avoiding unnecessary energy waste. At the same time, by combining the monitoring of the real-time temperature change rate, the heat storage amount is finely adjusted to make the heat pump operate in the optimal energy efficiency state. It improves the adaptability of the heat pump to sudden weather changes, reduces energy consumption, and ensures the comfort of indoor temperature control.

[0108] Those skilled in the art should understand that the embodiments of the present application can be provided as a method, a system, or a computer program product. Therefore, the present application can take the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can 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.) that contain computer-usable program code.

[0109] The present application is described with reference to the flowcharts and / or block diagrams of methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each flow and / or block in the flowcharts and / or block diagrams can be implemented by computer program instructions, and the combination of the flows and / or blocks in the flowcharts and / or block diagrams can also be implemented by computer program instructions. These computer program instructions can be provided to the processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing devices to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing devices generate for implementing in the process Figure 1one or more processes and / or blocks Figure 1 means for the functions specified in one or more blocks

[0110] These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to work in a particular manner, such that the instructions stored in the computer-readable memory produce a manufacture including an instruction means that implements the functions in the process Figure 1 one or more processes and / or blocks Figure 1 specified in one or more blocks

[0111] These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus, such that a series of operation steps are executed on the computer or other programmable apparatus to produce a computer-implemented process, so that the instructions executed on the computer or other programmable apparatus provide steps for implementing the functions specified in the process Figure 1 one or more processes and / or blocks Figure 1 specified in one or more blocks

[0112] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the above embodiments, those of ordinary skill in the art should understand that: the specific embodiments of the present invention can still be modified or equivalently replaced, and any modification or equivalent replacement that does not depart from the spirit and scope of the present invention shall be covered by the protection scope of the claims of the present invention.

Claims

1. An energy-saving control method for a heat pump, characterized in that, Including: Collecting meteorological data within a preset area for storage, determining whether to perform preheating control according to the meteorological data, when it is determined to perform the preheating control, determining a cooling center point according to the meteorological data, and determining a neighborhood boundary range according to the meteorological data of the cooling center point; Obtaining forecast data of each point within the neighborhood boundary range based on the minute-level grid cooling forecast and the CMA-MESO cooling forecast; Obtaining historical data of each point within the neighborhood boundary range, combining the historical data with the 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 warning information of the cooling center point according to the peak density map, where the cooling warning information includes the cooling time and the warning level; Determining the heat storage amount according to the warning level, collecting the real-time temperature change rate within the preset area before the cooling time, and judging whether to adjust the heat storage amount according to the temperature change rate; When determining whether to perform preheating control according to the meteorological data, it includes: Comparing the meteorological data of each point within the preset area with a data threshold respectively, and determining 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, it is determined to perform the preheating control; When the temperature data is greater than or equal to the temperature threshold, it is determined not to perform the preheating control.

2. The energy-saving control method for a heat pump according to claim 1, characterized in that, When determining the neighborhood boundary range according to the meteorological data of the cooling center point, it includes: Selecting the points corresponding to the meteorological data greater than the data threshold within the preset area as the cooling center point, and determining the neighborhood boundary range according to the real-time meteorological data of the cooling center point, where the neighborhood boundary range is inversely proportional to the real-time meteorological data.

3. The energy-saving control method for a heat pump according to claim 2, wherein When obtaining the forecast data of each point within the neighborhood boundary range based on the minute-level grid cooling forecast and the CMA-MESO cooling forecast, it includes: Obtaining the forecast data of 0-2h within the neighborhood boundary range based on the minute-level grid cooling forecast; Obtaining the forecast data of 2-12h within the neighborhood boundary range based on the CMA-MESO cooling forecast; Using the nearest neighbor interpolation method to convert the forecast data of 2-12h within the neighborhood boundary range from a resolution of 3KM×3KM to 1KM×1KM; Fusing the converted forecast data of 2-12h within the neighborhood boundary range with the forecast data of 0-2h within the neighborhood boundary range to obtain the forecast data of 0-12h within the neighborhood boundary range.

4. The energy-saving control method for a heat pump according to claim 3, characterized in that When combining the historical data with the forecast data to obtain a combined data set, it includes: Obtaining the meteorological data of the past 3h within the neighborhood boundary range according to the historical data; Obtaining the forecast data of the next 3h according to the forecast data of 0-12h within the neighborhood boundary range; Combining the meteorological data of the past 3h with the forecast data of the next 3h to obtain the combined data set.

5. The energy-saving control method for a heat pump according to claim 4, wherein 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 dataset, h represents the smoothing bandwidth, s i represents the i-th data point in the combined dataset, and x represents any variable in the overall data; Where the smoothing bandwidth is calculated by the following formula: ； Wherein, h represents the smoothing bandwidth, σ represents the standard deviation of the data in the combined dataset, β1 represents the skewness of the data, β2 represents the kurtosis of the data, n represents the number of data samples in the combined dataset, and k represents the adjustment coefficient; Wherein, the skewness of the data is obtained by calculating with the following formula: ； The kurtosis of the data is obtained by calculating with the following formula: ； where n represents the number of data samples in the combined dataset, and s i represents the i-th data point in the combined dataset, represents the mean of the data in the combined dataset, and σ represents the standard deviation of the data in the combined dataset.

6. The energy-saving control method for a heat pump according to claim 5, characterized in that, When determining the cooling warning information of the cooling center point according to the peak density map, it includes: Obtaining the meteorological data difference based on the meteorological data corresponding to the highest value of the density estimate in the peak density map and the data threshold, obtaining the cooling moment based on the meteorological data corresponding to the highest value of the density estimate in the peak density map and the forecast data, comparing the meteorological data difference with the first preset difference and the second preset difference respectively, and determining 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 the first preset difference, determining the first cooling level as the warning level; when the meteorological data difference is greater than the first preset difference and less than or equal to the second preset difference, determining the second cooling level as the warning level; when the meteorological data difference is greater than the second preset difference, determining the third cooling level as the warning level; the first cooling level indicates that the cooling degree is higher than the second cooling level, and the second cooling level indicates that the cooling degree is higher than the third cooling level.

7. The energy-saving control method for a heat pump according to claim 6, characterized in that When collecting the real-time temperature change rate in the preset area before the cooling moment and judging whether to adjust the heat storage amount according to the temperature change rate, it includes: Obtaining the predicted temperature change rate based on the meteorological data corresponding to the highest value of the density estimate in the peak density map and the cooling moment, comparing the temperature change rate with the predicted temperature change rate, and judging whether to adjust the heat storage amount according to the comparison result.

8. The energy-saving control method for a heat pump according to claim 7, 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 to adjust the heat storage amount, 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 amount, and the adjustment coefficient is in a direct proportion relationship with 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 not to adjust the heat storage amount.

9. An energy-saving control system for a heat pump, which is used to apply the energy-saving control method for a heat pump according to any one of claims 1-8, characterized in that, It includes: A heat storage module configured to store a high-temperature medium; A control module, including a collection unit, a processing unit, an analysis unit, and an adjustment unit, wherein, The collection unit is configured to collect and store the meteorological data in the preset area, judge whether to perform preheating control according to the meteorological data, when it is determined to perform the preheating control, determine the cooling center point according to the meteorological data, and determine the neighborhood boundary range according to the meteorological data of the cooling center point; The processing unit is configured to obtain the forecast data of each point within the neighborhood boundary range based on the minute-level grid cooling forecast and the CMA-MESO cooling forecast; The parsing unit is configured to obtain the 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 based on the combined data set, draw a peak density map according to the Gaussian kernel density function, and determine the cooling warning information of the cooling center point according to the peak density map. The cooling warning information includes the cooling time and the warning level; 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 cooling time, and determine whether to adjust the heat storage amount according to the temperature change rate.

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