Energy consumption evaluation method and system for heat pump system

Abstract

The invention relates to the technical field of energy consumption assessment, and discloses an energy consumption assessment method and system for a heat pump system, and the method comprises the steps: determining a current energy consumption value according to output temperature data and indoor temperature data, obtaining historical output temperature data and historical indoor temperature data, and determining a predicted energy consumption value based on a random forest model, analyzing each incrustation image to determine the comprehensive quantity of incrustation of the evaporator, comparing the comprehensive quantity of incrustation with historical incrustation data, judging whether the comprehensive quantity of incrustation is correct or not according to a comparison result, and when the incrustation similarity is greater than or equal to an incrustation similarity threshold, determining the predicted quantity of incrustation according to the historical incrustation data; and when the scale similarity is smaller than a scale similarity threshold, determining a scale prediction number according to the scale correction set, and determining a target prediction energy consumption value according to the energy consumption correction coefficient and the prediction energy consumption value. According to the method, the water scale of the evaporator is analyzed, so that the reliability of energy consumption evaluation of the heat pump system is improved.

Description

Technical Field

[0001] The present invention relates to the technical field of energy consumption assessment, and in particular, to an energy consumption assessment method and system for a heat pump system. Background Art

[0002] With the development of the population, the heating requirements for the indoor environment in residential areas are increasing day by day. A heat pump system is a system that converts energy using water resources, and its energy consumption lies in the heat exchange made with electric energy. Therefore, how to accurately evaluate the energy consumption is crucial for optimizing the operating efficiency of the heat pump system and reducing energy waste.

[0003] Currently, the energy consumption assessment of heat pump systems mainly relies on the judgment of manual experience. Manual experience can lead to insufficient accuracy of the energy consumption assessment results due to different experiences of different detection personnel. In addition, when evaluating the energy consumption of heat pump systems through manual experience, the impact of scale on the evaporator during the circulation process will be ignored. The accumulation of scale will reduce the heat conversion efficiency, resulting in the heat pump system needing to consume more energy to maintain the same output heat. The existing energy consumption assessment does not effectively analyze the scale, resulting in inaccurate energy consumption assessment results and unable to effectively predict the energy consumption of the heat pump system.

[0004] Therefore, how to provide an energy consumption assessment method and system for a heat pump system is an urgent technical problem to be solved by those skilled in the art. Summary of the Invention

[0005] In view of this, the present invention proposes an energy consumption assessment method and system for a heat pump system, aiming to solve the problems that relying on the judgment of manual experience will lead to insufficient accuracy of the energy consumption assessment results, ineffective analysis of scale, resulting in inaccurate energy consumption assessment results, and inability to effectively predict the energy consumption of the heat pump system.

[0006] On the one hand, the present invention proposes an energy consumption assessment method for a heat pump system, including: Obtain the output temperature data of the heat pump system and the indoor temperature data of the residential area, determine the current energy consumption value according to the output temperature data and the indoor temperature data, obtain the historical output temperature data and historical indoor temperature data, and determine the predicted energy consumption value based on the random forest model; Divide the evaporator into several scale detection areas, obtain the scale images of each scale detection area, analyze each scale image to determine the comprehensive scale quantity of the evaporator, compare the comprehensive scale quantity with the historical scale data, and judge whether the comprehensive scale quantity is correct according to the comparison result. When it is determined that the comprehensive scale quantity is incorrect, determine the scale similarity based on the comprehensive scale quantity and the historical scale data; When the scale similarity is greater than or equal to the scale similarity threshold, determine the predicted scale quantity according to the historical scale data. When the scale similarity is less than the scale similarity threshold, perform set partitioning on the historical scale data to determine the scale correction set, and determine the predicted scale quantity according to the scale correction set; Set the energy consumption correction coefficient of the predicted energy consumption value according to the predicted scale quantity, and determine the target predicted energy consumption value according to the energy consumption correction coefficient and the predicted energy consumption value.

[0007] Further, when obtaining the output temperature data of the heat pump system and the indoor temperature data of the residential area, and determining the current energy consumption value according to the output temperature data and the indoor temperature data, it includes: Obtain the power input value and the output temperature value of the heat pump system, and obtain the average indoor temperature of the residential area; The current energy consumption value is obtained by the following formula: ; Where, Z1 represents the current energy consumption value, P1 represents the power input value of the heat pump system, T1 represents the output temperature value, and T2 represents the average indoor temperature.

[0008] Further, when obtaining the historical output temperature data and the historical indoor temperature data and determining the predicted energy consumption value based on the random forest model, it includes: Construct the historical output temperature data and the historical indoor temperature data into a data set, and divide the data set into a training set and a test set. Use grid search to find the establishment parameters of the random forest model, establish the random forest model, fit the random forest model with the training set, bring the test set into the random forest model and calculate the accuracy of the predicted energy consumption value; When the accuracy reaches the preset accuracy threshold, substitute the current energy consumption value into the random forest model to determine the predicted energy consumption value.

[0009] Further, when obtaining the scale images of each scale detection area and analyzing each scale image to determine the comprehensive scale quantity of the evaporator, it includes: Preprocess the scale images of each scale detection area. The preprocessing includes image denoising and resizing the image, and determine the processed scale images of each scale detection area according to the results of the preprocessing; Extract the scale pixel points of all processed scale images, determine the scale pixel features of each scale pixel point, and obtain the feature mean of the scale pixel features; Divide the scale deposit pixel points with scale deposit pixel features greater than the feature mean into the first scale deposit sequence, divide the scale deposit pixel points with scale deposit pixel features equal to the feature mean into the second scale deposit sequence, and divide the scale deposit pixel points with scale deposit pixel features less than the feature mean into the third scale deposit sequence; Count the first scale deposit quantity of the scale deposit pixel points in the first scale deposit sequence, count the second scale deposit quantity of the scale deposit pixel points in the second scale deposit sequence, and the comprehensive scale deposit quantity of the evaporator is the sum of the first scale deposit quantity and the second scale deposit quantity.

[0010] Further, when comparing the comprehensive scale deposit quantity with historical scale deposit data and judging whether the comprehensive scale deposit quantity is correct according to the comparison result, it includes: The historical scale deposit data includes the maximum value of the historical comprehensive scale deposit quantity, the mean value of the historical comprehensive scale deposit quantity, and all historical comprehensive scale deposit quantities; When the comprehensive scale deposit quantity is greater than or equal to the maximum value of the historical comprehensive scale deposit quantity, it is determined that the comprehensive scale deposit quantity is correct, and the comprehensive scale deposit quantity is determined as the predicted scale deposit quantity; When the comprehensive scale deposit quantity is less than the maximum value of the historical comprehensive scale deposit quantity, it is determined that the comprehensive scale deposit quantity is incorrect.

[0011] Further, when determining the scale deposit similarity based on the comprehensive scale deposit quantity and the historical scale deposit data, it includes: The scale deposit similarity is obtained by the following formula: ; Where, S represents the scale deposit similarity, G1 represents the comprehensive scale deposit quantity, G2 represents the maximum value of the historical comprehensive scale deposit quantity, and G3 represents the mean value of the historical comprehensive scale deposit quantity.

[0012] Further, when the scale deposit similarity is greater than or equal to the scale deposit similarity threshold, determine the predicted scale deposit quantity according to the historical scale deposit data. When the scale deposit similarity is less than the scale deposit similarity threshold, divide the historical scale deposit data into a set to determine the scale deposit correction set, and determine the predicted scale deposit quantity according to the scale deposit correction set, it includes: Preset the scale deposit similarity threshold; When the scale deposit similarity is greater than or equal to the scale deposit similarity threshold, determine the maximum value of the historical comprehensive scale deposit quantity as the predicted scale deposit quantity; The scale deposit correction set includes a first scale deposit correction set, a second scale deposit correction set, and a third scale deposit correction set; When the scale similarity is less than the scale similarity threshold, the historical scale comprehensive quantities greater than the average value of the historical scale comprehensive quantities are classified into the first scale correction set, the historical scale comprehensive quantities equal to the average value of the historical scale comprehensive quantities are classified into the second scale correction set, and the historical scale comprehensive quantities less than the average value of the historical scale comprehensive quantities are classified into the third scale correction set; Determine the predicted scale quantity according to the first scale correction set, the second scale correction set, and the third scale correction set.

[0013] Further, when determining the predicted scale quantity according to the first scale correction set, the second scale correction set, and the third scale correction set, it includes: The predicted scale quantity is obtained by the following formula: ; where K represents the predicted scale quantity, G2 represents the maximum value of the historical scale comprehensive quantity, Y1 represents the maximum value of the historical scale comprehensive quantity in the first scale correction set, Y2 represents the minimum value of the historical scale comprehensive quantity in the first scale correction set, U1 represents the maximum value of the historical scale comprehensive quantity in the second scale correction set, U2 represents the minimum value of the historical scale comprehensive quantity in the second scale correction set, I1 represents the maximum value of the historical scale comprehensive quantity in the third scale correction set, and I2 represents the minimum value of the historical scale comprehensive quantity in the third scale correction set.

[0014] Further, when setting the energy consumption correction coefficient of the predicted energy consumption value according to the predicted scale quantity and determining the target predicted energy consumption value according to the energy consumption correction coefficient and the predicted energy consumption value, it includes: Preset a first preset predicted scale quantity and a second preset predicted scale quantity, where the first preset predicted scale quantity is greater than the second preset predicted scale quantity; Preset a first preset energy consumption correction coefficient, a second preset energy consumption correction coefficient, and a third preset energy consumption correction coefficient, where the first preset energy consumption correction coefficient is greater than the second preset energy consumption correction coefficient, and the second preset energy consumption correction coefficient is greater than the third preset energy consumption correction coefficient; When the predicted scale quantity is greater than the first preset predicted scale quantity, then use the first preset energy consumption correction coefficient as the energy consumption correction coefficient of the predicted energy consumption value; When the predicted scale quantity is less than or equal to the first preset predicted scale quantity and greater than the second preset predicted scale quantity, then use the second preset energy consumption correction coefficient as the energy consumption correction coefficient of the predicted energy consumption value; When the predicted scale quantity is less than or equal to the second preset predicted scale quantity, the third preset energy consumption correction coefficient is used as the energy consumption correction coefficient of the predicted energy consumption value; The target predicted energy consumption value is the product of the predicted energy consumption value and the energy consumption correction coefficient.

[0015] Compared with the prior art, the beneficial effects of the present invention are as follows: By obtaining historical output temperature data and historical indoor temperature data, the accurate evaluation of the energy consumption of the heat pump system is realized by using machine learning technology. The scale image is subjected to image analysis and the historical scale data is comprehensively utilized to dynamically adjust the comprehensive scale quantity. When there is a certain deviation in the scale similarity, the historical scale data is divided into sets to determine the scale correction set, and the predicted scale quantity can be accurately obtained, avoiding the risk of incorrect energy consumption evaluation caused by human neglect of scale, thereby improving the accuracy of the energy consumption evaluation of the heat pump system and effectively improving the reliability of the energy consumption prediction of the heat pump system.

[0016] On the other hand, the present application also provides an energy consumption evaluation system for a heat pump system, which is used to apply the above-mentioned energy consumption evaluation method for a heat pump system, including: An energy consumption acquisition module, configured to acquire the output temperature data of the heat pump system and the indoor temperature data of the residential area, determine the current energy consumption value according to the output temperature data and the indoor temperature data, acquire the historical output temperature data and historical indoor temperature data, and determine the predicted energy consumption value based on the random forest model; An energy consumption analysis module, configured to divide the evaporator into several scale detection areas, acquire the scale images of each scale detection area, analyze each scale image to determine the comprehensive scale quantity of the evaporator, compare the comprehensive scale quantity with the historical scale data, and judge whether the comprehensive scale quantity is correct according to the comparison result. When it is determined that the comprehensive scale quantity is incorrect, determine the scale similarity based on the comprehensive scale quantity and the historical scale data; An energy consumption processing module, configured to determine the predicted scale quantity according to the historical scale data when the scale similarity is greater than or equal to the scale similarity threshold, and divide the historical scale data into sets to determine the scale correction set when the scale similarity is less than the scale similarity threshold, and determine the predicted scale quantity according to the scale correction set; An energy consumption determination module, configured to set the energy consumption correction coefficient of the predicted energy consumption value according to the predicted scale quantity, and determine the target predicted energy consumption value according to the energy consumption correction coefficient and the predicted energy consumption value.

[0017] It can be understood that the above-mentioned energy consumption evaluation method and system for a heat pump system have the same beneficial effects, which will not be elaborated here. Brief Description of the Drawings

[0018] Upon reading the following detailed description of the preferred embodiments, various other advantages and benefits will become apparent 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: Figure 1 is a flowchart of an energy consumption evaluation method for a heat pump system provided by an embodiment of the present invention; Figure 2 is a functional block diagram of an energy consumption evaluation system for a heat pump system provided by an embodiment of the present invention. Specific Embodiments

[0019] Exemplary embodiments of the present disclosure will be described in more detail below with reference to the accompanying 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.

[0020] Referring to Figure 1 as shown, in some embodiments of the present application, the present embodiment provides an energy consumption evaluation method for a heat pump system, including: S100: Obtain the output temperature data of the heat pump system and the indoor temperature data of the residential area, determine the current energy consumption value according to the output temperature data and the indoor temperature data, obtain the historical output temperature data and the historical indoor temperature data, and determine the predicted energy consumption value based on the random forest model.

[0021] S200: Divide the evaporator into several scale detection areas, obtain the scale images of each scale detection area, analyze each scale image to determine the comprehensive scale quantity of the evaporator, compare the comprehensive scale quantity with the historical scale data, and judge whether the comprehensive scale quantity is correct according to the comparison result. When it is determined that the comprehensive scale quantity is incorrect, determine the scale similarity based on the comprehensive scale quantity and the historical scale data.

[0022] S300: When the scale similarity is greater than or equal to the scale similarity threshold, determine the predicted scale quantity according to the historical scale data. When the scale similarity is less than the scale similarity threshold, divide the historical scale data to determine the scale correction set, and determine the predicted scale quantity according to the scale correction set.

[0023] S400: Set an energy consumption correction coefficient for the predicted energy consumption value according to the predicted scale deposit quantity, and determine the target predicted energy consumption value based on the energy consumption correction coefficient and the predicted energy consumption value.

[0024] Specifically, obtain the output temperature data of the heat pump system and the indoor temperature data of the residential area to determine the current energy consumption value, which reflects the actual energy consumption of the heat pump system. The output temperature data of the heat pump system represents the temperature value actually output for the residential area. Since the current energy consumption value represents the energy consumption determined under the current circumstances and cannot reflect the overall energy consumption of the heat pump system, historical output temperature data and historical indoor temperature data are obtained and the predicted energy consumption value is determined based on the random forest model, avoiding the energy consumption error caused by relying on data at a single time and improving the accuracy of energy consumption evaluation. The predicted energy consumption value represents the comprehensive energy consumption of the predicted heat pump system. During the heat energy conversion process of the heat pump system, a certain amount of scale deposits will be generated around the evaporator, and the scale deposits will increase the energy consumption of the heat pump system. The evaporator is divided into several scale deposit detection areas, preferably 10, which can be adjusted according to the actual size of the evaporator. Use image acquisition devices such as cameras to collect scale deposit images for each scale deposit detection area, and use image algorithms to analyze these images to determine the comprehensive scale deposit quantity of the evaporator. Compare the obtained comprehensive scale deposit quantity with the historical scale deposit data, and judge whether the comprehensive scale deposit quantity is correct according to the comparison result, avoiding the error caused by accidental data and further improving the accuracy of energy consumption evaluation, effectively improving the reliability of the energy consumption prediction of the heat pump system.

[0025] It can be understood that when the scale deposit similarity is greater than or equal to the scale deposit similarity threshold, it is considered that the difference from the historical scale deposit data is not obvious, and the historical scale deposit data can be directly used to determine the predicted scale deposit quantity. When the scale deposit similarity is less than the scale deposit similarity threshold, it means that the difference from the historical scale deposit data is relatively obvious, and the historical scale deposit data needs to be further analyzed to obtain a scale deposit correction set, and then the predicted scale deposit quantity is determined according to the scale deposit correction set. Since the presence of scale deposits will affect heat exchange and thus affect the predicted energy consumption value, setting an energy consumption correction coefficient for the predicted energy consumption value according to the predicted scale deposit quantity can obtain an accurate target predicted energy consumption value, thereby improving the accuracy of energy consumption evaluation and further improving the energy management of the heat pump system.

[0026] In some embodiments of the present application, when obtaining the output temperature data of the heat pump system and the indoor temperature data of the residential area and determining the current energy consumption value according to the output temperature data and the indoor temperature data, it includes: obtaining the power input value and the output temperature value of the heat pump system, obtaining the average indoor temperature of the residential area, and the current energy consumption value is obtained by the following formula: ; Among them, Z1 represents the current energy consumption value, P1 represents the power input value of the heat pump system, T1 represents the output temperature value, and T2 represents the average indoor temperature.

[0027] Specifically, the average indoor temperature in the residential area is expressed as the average temperature of the indoor temperatures of all users in the local residential area. The power input value of the heat pump system can be obtained by referring to the actual power input of the local heat pump system. Similarly, for the output temperature value, referring to the local heat pump system can obtain the actual output temperature. By calculating the current energy consumption value, the current energy consumption situation of the heat pump system can be comprehensively evaluated, improving the reliability and accuracy of energy consumption assessment and laying a data foundation for subsequent prediction.

[0028] In some embodiments of the present application, when obtaining historical output temperature data and historical indoor temperature data and determining the predicted energy consumption value based on the random forest model, it includes: constructing the historical output temperature data and historical indoor temperature data into a data set, dividing the data set into a training set and a test set, using grid search to find the establishment parameters of the random forest model, establishing the random forest model, using the training set to fit the random forest model, substituting the test set into the random forest model and calculating the accuracy rate of the predicted energy consumption value. When the accuracy rate reaches the preset accuracy rate threshold, substituting the current energy consumption value into the random forest model to determine the predicted energy consumption value.

[0029] Specifically, the data set contains data of the heat pump system at different times and indoor temperature data at different times. 70% to 90% of the data in the data set is used as the training set, and the rest is used as the test set. Ensure that both the training set and the test set contain data of multiple periods to improve the generalization ability of the model. Grid search exhaustively searches for the establishment parameters of the random forest model in the parameter space to establish the random forest model. Moreover, using the training set to fit the random forest model reduces the risk of overfitting and improves the accuracy and stability of the random forest model. Substitute the test set into the random forest model to calculate the prediction accuracy rate of the model. The accuracy rate reflects the performance of the model on unknown data and is an index for evaluating the model performance. After the model reaches the preset accuracy rate threshold, substituting the current energy consumption value into the random forest model to determine the predicted energy consumption value avoids the risk of inaccurate energy consumption assessment caused by relying on manual experience judgment, and improves the accuracy and reliability of energy consumption prediction.

[0030] In some embodiments of the present application, when obtaining the scale images of each scale detection area and analyzing each scale image to determine the comprehensive scale quantity of the evaporator, it includes: preprocessing the scale images of each scale detection area, where the preprocessing includes image denoising and resizing the image, determining the processed scale images of each scale detection area according to the results of the preprocessing, extracting the scale pixel points of all the processed scale images, and determining the scale pixel features of each scale pixel point, obtaining the feature mean value of the scale pixel features, dividing the scale pixel points with scale pixel features greater than the feature mean value into the first scale sequence, dividing the scale pixel points with scale pixel features equal to the feature mean value into the second scale sequence, dividing the scale pixel points with scale pixel features less than the feature mean value into the third scale sequence, counting the first scale quantity of the scale pixel points in the first scale sequence, counting the second scale quantity of the scale pixel points in the second scale sequence, and the comprehensive scale quantity of the evaporator is the sum of the first scale quantity and the second scale quantity.

[0031] Specifically, after obtaining the scale images of image acquisition devices such as cameras, there will be certain noise interference or other interference factors in the scale images, which affect the image quality. Image denoising can remove the random noise in the image and ensure the image quality, while resizing the image is to unify the image resolution, reduce the complexity and computational amount of image processing, and lay an image foundation for subsequent extraction. After preprocessing the images of each scale detection area, the scale pixel features of each scale pixel point include hue, saturation, brightness, etc. Obtaining the feature mean value of the scale pixel features to get a standardized reference value can divide each pixel point into sequences. The first scale sequence, the second scale sequence, and the third scale sequence represent the scale conditions of different degrees of the evaporator. The scale pixel points with larger scale pixel features indicate a more serious scale situation, and the scale pixel points with smaller scale pixel features indicate a lighter scale situation, which can almost be ignored. The sum of the first scale quantity and the second scale quantity can reflect the actual scale quantity around the evaporator and provide data support for subsequent energy consumption evaluation.

[0032] It can be understood that traditional manual detection is affected by factors such as human experience, resulting in errors. By obtaining images and performing preprocessing, the reliability of energy consumption evaluation is improved, and each scale pixel point is carefully divided. Especially in the case of complex scale distribution, the accuracy of scale quantity statistics is ensured, thereby improving the reliability of the comprehensive scale quantity.

[0033] In some embodiments of the present application, when comparing the comprehensive scale deposit quantity with the historical scale deposit data and determining whether the comprehensive scale deposit quantity is correct according to the comparison result, it includes: The historical scale deposit data includes the maximum value of the historical comprehensive scale deposit quantity, the average value of the historical comprehensive scale deposit quantity, and all the historical comprehensive scale deposit quantities. When the comprehensive scale deposit quantity is greater than or equal to the maximum value of the historical comprehensive scale deposit quantity, it is determined that the comprehensive scale deposit quantity is correct, and the comprehensive scale deposit quantity is determined as the predicted scale deposit quantity. When the comprehensive scale deposit quantity is less than the maximum value of the historical comprehensive scale deposit quantity, it is determined that the comprehensive scale deposit quantity is incorrect.

[0034] Specifically, the maximum value of the historical comprehensive scale deposit quantity in the historical scale deposit data represents the maximum value of the scale deposits existing in the evaporator at different times. The average value of the historical comprehensive scale deposit quantity in the historical scale deposit data represents the average value of the scale deposit quantity during the operation of the evaporator at different times. During the operation of the heat pump system, the scale deposits will not disappear without being removed artificially, and with the operation of the heat pump system, the scale deposits will gradually accumulate. If the comprehensive scale deposit quantity is less than the maximum value of the historical comprehensive scale deposit quantity, it indicates that there is a certain deviation in the statistical result, and the comprehensive scale deposit quantity needs to be adjusted. When the comprehensive scale deposit quantity is greater than or equal to the maximum value of the historical comprehensive scale deposit quantity, it indicates that it conforms to the situation of scale deposit formation, and the comprehensive scale deposit quantity is not adjusted, and the comprehensive scale deposit quantity is directly determined as the predicted scale deposit quantity. By judging the comprehensive scale deposit quantity, it lays a foundation for determining the energy consumption correction coefficient subsequently.

[0035] In some embodiments of the present application, when determining the scale deposit similarity based on the comprehensive scale deposit quantity and the historical scale deposit data, it includes: The scale deposit similarity is obtained by the following formula: ; where S represents the scale deposit similarity, G1 represents the comprehensive scale deposit quantity, G2 represents the maximum value of the historical comprehensive scale deposit quantity, and G3 represents the average value of the historical comprehensive scale deposit quantity.

[0036] In some embodiments of the present application, when the scale similarity is greater than or equal to the scale similarity threshold, the predicted scale quantity is determined based on historical scale data. When the scale similarity is less than the scale similarity threshold, the historical scale data is grouped to determine a scale correction set. When determining the predicted scale quantity based on the scale correction set, it includes: presetting the scale similarity threshold. When the scale similarity is greater than or equal to the scale similarity threshold, the maximum value of the historical comprehensive scale quantity is determined as the predicted scale quantity. The scale correction set includes a first scale correction set, a second scale correction set, and a third scale correction set. When the scale similarity is less than the scale similarity threshold, the historical comprehensive scale quantities greater than the average value of the historical comprehensive scale quantities are grouped into the first scale correction set, the historical comprehensive scale quantities equal to the average value of the historical comprehensive scale quantities are grouped into the second scale correction set, and the historical comprehensive scale quantities less than the average value of the historical comprehensive scale quantities are grouped into the third scale correction set. The predicted scale quantity is determined based on the first scale correction set, the second scale correction set, and the third scale correction set.

[0037] In some embodiments of the present application, when determining the predicted scale quantity based on the first scale correction set, the second scale correction set, and the third scale correction set, it includes: the predicted scale quantity is obtained by the following formula: ; where K represents the predicted scale quantity, G2 represents the maximum value of the historical comprehensive scale quantity, Y1 represents the maximum value of the historical comprehensive scale quantity in the first scale correction set, Y2 represents the minimum value of the historical comprehensive scale quantity in the first scale correction set, U1 represents the maximum value of the historical comprehensive scale quantity in the second scale correction set, U2 represents the minimum value of the historical comprehensive scale quantity in the second scale correction set, I1 represents the maximum value of the historical comprehensive scale quantity in the third scale correction set, and I2 represents the minimum value of the historical comprehensive scale quantity in the third scale correction set.

[0038] Specifically, when the scale similarity is greater than or equal to the preset scale similarity threshold, it indicates that the comprehensive scale quantity and the historical scale data have a high similarity, so it is considered that the comprehensive scale quantity is relatively accurate, and the maximum value of the historical comprehensive scale quantity is directly used as the predicted scale quantity. When the scale similarity is less than the preset scale similarity threshold, it indicates that the comprehensive scale quantity and the historical scale data differ greatly, and the historical scale data needs to be grouped to determine a scale correction set to further refine the historical scale data. The historical scale data covers all historical comprehensive scale quantities of the evaporator in different periods. By establishing a scale correction set based on the relationship between the average value of the historical comprehensive scale quantity and the historical comprehensive scale quantity, the distribution of the historical comprehensive scale quantity can be determined, thereby improving the accuracy and reliability of the predicted scale quantity.

[0039] In some embodiments of the present application, when setting the energy consumption correction coefficient for the predicted energy consumption value according to the predicted scale quantity and determining the target predicted energy consumption value based on the energy consumption correction coefficient and the predicted energy consumption value, the following steps are included: presetting a first preset scale prediction quantity and a second preset scale prediction quantity, where the first preset scale prediction quantity is greater than the second preset scale prediction quantity; presetting a first preset energy consumption correction coefficient, a second preset energy consumption correction coefficient, and a third preset energy consumption correction coefficient, where the first preset energy consumption correction coefficient is greater than the second preset energy consumption correction coefficient, and the second preset energy consumption correction coefficient is greater than the third preset energy consumption correction coefficient. When the predicted scale quantity is greater than the first preset scale prediction quantity, the first preset energy consumption correction coefficient is used as the energy consumption correction coefficient for the predicted energy consumption value. When the predicted scale quantity is less than or equal to the first preset scale prediction quantity and greater than the second preset scale prediction quantity, the second preset energy consumption correction coefficient is used as the energy consumption correction coefficient for the predicted energy consumption value. When the predicted scale quantity is less than or equal to the second preset scale prediction quantity, the third preset energy consumption correction coefficient is used as the energy consumption correction coefficient for the predicted energy consumption value. The target predicted energy consumption value is the product of the predicted energy consumption value and the energy consumption correction coefficient.

[0040] Specifically, by setting the first preset scale prediction quantity, the second preset scale prediction quantity, and the corresponding energy consumption correction coefficients, the set scale prediction quantities take into account the scale effects of different degrees, effectively evaluating the impact of the evaporator on the predicted energy consumption value under the condition of scale accumulation. Thus, the corresponding energy consumption correction coefficient is dynamically selected according to the scale condition of the evaporator, and then an accurate target predicted energy consumption value is obtained. Especially when the accumulated scale quantity is large, it avoids the situation that a single standard energy consumption correction coefficient cannot fully reflect the target predicted energy consumption value of the heat pump system, and avoids the deviation in evaluation caused by the judgment of human experience, improving the accuracy and reliability of energy consumption evaluation.

[0041] In summary, the beneficial effects of the present invention are as follows: By obtaining the historical output temperature data and the historical indoor temperature data, the accurate evaluation of the energy consumption of the heat pump system is realized by using machine learning technology. The scale images are subjected to image analysis and the historical scale data is comprehensively utilized to dynamically adjust the comprehensive scale quantity. When there is a certain deviation in the scale similarity, the historical scale data is divided into sets to determine the scale correction set, and the predicted scale quantity can be accurately obtained, avoiding the risk of incorrect energy consumption evaluation caused by human neglect of scale, thereby improving the accuracy of the energy consumption evaluation of the heat pump system and effectively enhancing the reliability of the energy consumption prediction of the heat pump system.

[0042] In another preferred manner based on the above embodiments, as shown in Figure 2 this embodiment provides an energy consumption evaluation system for a heat pump system, which is used to apply the above energy consumption evaluation method for a heat pump system, and includes: An energy consumption acquisition module, configured to acquire output temperature data of a heat pump system and indoor temperature data of a residential area, determine a current energy consumption value according to the output temperature data and the indoor temperature data, acquire historical output temperature data and historical indoor temperature data, and determine a predicted energy consumption value based on a random forest model; An energy consumption analysis module, configured to divide an evaporator into a plurality of scale detection zones, acquire scale images of each scale detection zone, analyze each scale image to determine a comprehensive scale quantity of the evaporator, compare the comprehensive scale quantity with historical scale data, and judge whether the comprehensive scale quantity is correct according to the comparison result. When it is determined that the comprehensive scale quantity is incorrect, determine a scale similarity based on the comprehensive scale quantity and the historical scale data; An energy consumption processing module, configured to determine a predicted scale quantity according to historical scale data when the scale similarity is greater than or equal to a scale similarity threshold, and when the scale similarity is less than the scale similarity threshold, perform set partitioning on the historical scale data to determine a scale correction set, and determine a predicted scale quantity according to the scale correction set; An energy consumption determination module, configured to set an energy consumption correction coefficient for a predicted energy consumption value according to the predicted scale quantity, and determine a target predicted energy consumption value according to the energy consumption correction coefficient and the predicted energy consumption value.

[0043] Specifically, the energy consumption acquisition module is responsible for determining the current energy consumption value according to the output temperature data of the heat pump system and the indoor temperature data of the residential area. Moreover, by acquiring the historical output temperature data and the historical indoor temperature data and determining the predicted energy consumption value based on the random forest model, the comprehensiveness of the energy consumption evaluation of the heat pump system is improved, the influence of the error of single data on the overall evaluation is avoided, and the use of the random forest model for prediction avoids the accumulation of errors caused by the judgment of human experience. During the process of circulating cooling water, due to the presence of salts and various microorganisms in the water, after the heat pump system operates for a period of time, scale will form on the surface of the evaporator, resulting in a reduction in heat transfer efficiency, thereby affecting the energy consumption of the heat pump system. Through the energy consumption analysis module, quantitative analysis of the scale can be performed and compared with historical scale data, laying a data foundation for subsequent processing. When it is determined that the comprehensive scale quantity is incorrect, the system will determine the scale similarity according to the comprehensive scale quantity and the historical scale data. The scale similarity represents the difference between the comprehensive scale quantity and the historical scale data. If the scale similarity is greater than or equal to the scale similarity threshold, it means that the comprehensive scale quantity and the historical scale data have a small difference, and the historical scale data can be directly used to determine the predicted scale quantity. If the scale similarity is less than the scale similarity threshold, it means that the comprehensive scale quantity and the historical scale data have a large difference, and it is necessary to perform set partitioning on the historical scale data to determine the predicted scale quantity, improving the flexibility and pertinence of the system processing, ensuring the reliability of the predicted scale quantity, and further improving the accuracy of the energy consumption evaluation.

[0044] It is understandable that after obtaining the accurate predicted scale amount, the energy consumption determination module sets the energy consumption correction coefficient of the predicted energy consumption value, and determines the target predicted energy consumption value according to the energy consumption correction coefficient, effectively coping with the influence of scale on energy consumption assessment. It can not only predict the energy consumption of the heat pump system, but also analyze the influence of scale accumulation, so as to accurately obtain the target predicted energy consumption value, improving the accuracy and reliability of energy consumption assessment.

[0045] Those skilled in the art should understand that the embodiments of the present application can be provided as methods, systems or computer program products. Therefore, the present application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment combining software and hardware aspects. Moreover, the present application can adopt 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 code.

[0046] 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 flowchart and / or block diagram can be implemented by computer program instructions, and the combination of the flows and / or blocks in the flowchart and / or block diagram. 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 device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate for implementing in the process Figure 1 a process or multiple processes and / or blocks Figure 1 a device for the functions specified in one block or multiple blocks.

[0047] These computer program instructions can also be stored in a computer-readable storage that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer-readable storage generate a manufactured product including an instruction device, and the instruction device implements in the process Figure 1 a process or multiple processes and / or blocks Figure 1 a device for the functions specified in one block or multiple blocks.

[0048] These computer program instructions can also be loaded onto a computer or other programmable data processing device, so that a series of operation steps are executed on the computer or other programmable device to generate a computer-implemented process, so that the instructions executed on the computer or other programmable device provide steps for implementing the functions specified in the process Figure 1 a process or multiple processes and / or blocks Figure 1 a device for the functions specified in one block or multiple blocks.

[0049] 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: modifications or equivalent substitutions can still be made to the specific embodiments of the present invention, and any modification or equivalent substitution that does not depart from the spirit and scope of the present invention should be covered within the protection scope of the claims of the present invention.

Claims

1. An energy consumption evaluation method for a heat pump system, characterized in that Including: Obtain the output temperature data of the heat pump system and the indoor temperature data of the residential area, determine the current energy consumption value according to the output temperature data and the indoor temperature data, obtain the historical output temperature data and historical indoor temperature data, and determine the predicted energy consumption value based on the random forest model; Divide the evaporator into several scale detection areas, obtain the scale images of each scale detection area, analyze each scale image to determine the comprehensive scale quantity of the evaporator, compare the comprehensive scale quantity with the historical scale data, and judge whether the comprehensive scale quantity is correct according to the comparison result. When it is determined that the comprehensive scale quantity is incorrect, determine the scale similarity based on the comprehensive scale quantity and the historical scale data; When the scale similarity is greater than or equal to the scale similarity threshold, determine the predicted scale quantity according to the historical scale data. When the scale similarity is less than the scale similarity threshold, divide the historical scale data to determine the scale correction set, and determine the predicted scale quantity according to the scale correction set; Set the energy consumption correction coefficient of the predicted energy consumption value according to the predicted scale quantity, and determine the target predicted energy consumption value according to the energy consumption correction coefficient and the predicted energy consumption value.

2. The energy consumption evaluation method for a heat pump system according to claim 1, wherein, When obtaining the output temperature data of the heat pump system and the indoor temperature data of the residential area, and determining the current energy consumption value according to the output temperature data and the indoor temperature data, it includes: Obtain the power input value and output temperature value of the heat pump system, and obtain the average indoor temperature of the residential area; The current energy consumption value is obtained by the following formula: ； Where, Z1 represents the current energy consumption value, P1 represents the power input value of the heat pump system, T1 represents the output temperature value, and T2 represents the average indoor temperature.

3. The energy consumption evaluation method for a heat pump system according to claim 2, characterized in that When obtaining the historical output temperature data and historical indoor temperature data and determining the predicted energy consumption value based on the random forest model, it includes: Construct the historical output temperature data and the historical indoor temperature data into a data set, divide the data set into a training set and a test set, use grid search to find the establishment parameters of the random forest model, establish the random forest model, fit the random forest model with the training set, and bring the test set into the random forest model and calculate the accuracy rate of the predicted energy consumption value; When the accuracy rate reaches the preset accuracy threshold, substitute the current energy consumption value into the random forest model to determine the predicted energy consumption value.

4. The energy consumption evaluation method for a heat pump system according to claim 3, wherein When obtaining the scale images of each scale detection area and analyzing each scale image to determine the comprehensive scale quantity of the evaporator, it includes: Preprocess the scale images of each scale detection area. The preprocessing includes image denoising and resizing the image, and determine the scale processed image of each scale detection area according to the result of the preprocessing; Extract the scale pixel points of all scale processed images, determine the scale pixel features of each scale pixel point, and obtain the feature mean value of the scale pixel features; Divide the scale deposit pixel points with scale deposit pixel features greater than the feature mean into the first scale deposit sequence, divide the scale deposit pixel points with scale deposit pixel features equal to the feature mean into the second scale deposit sequence, and divide the scale deposit pixel points with scale deposit pixel features less than the feature mean into the third scale deposit sequence; Count the first scale deposit quantity of the scale deposit pixel points in the first scale deposit sequence, count the second scale deposit quantity of the scale deposit pixel points in the second scale deposit sequence, and the comprehensive scale deposit quantity of the evaporator is the sum of the first scale deposit quantity and the second scale deposit quantity.

5. The energy consumption evaluation method for a heat pump system according to claim 4, characterized in that, When comparing the comprehensive scale deposit quantity with historical scale deposit data and judging whether the comprehensive scale deposit quantity is correct according to the comparison result, it includes: The historical scale deposit data includes the maximum value of the historical comprehensive scale deposit quantity, the mean value of the historical comprehensive scale deposit quantity, and all historical comprehensive scale deposit quantities; When the comprehensive scale deposit quantity is greater than or equal to the maximum value of the historical comprehensive scale deposit quantity, it is determined that the comprehensive scale deposit quantity is correct, and the comprehensive scale deposit quantity is determined as the predicted scale deposit quantity; When the comprehensive scale deposit quantity is less than the maximum value of the historical comprehensive scale deposit quantity, it is determined that the comprehensive scale deposit quantity is incorrect.

6. The energy consumption evaluation method for a heat pump system according to claim 5, characterized in that When determining the scale deposit similarity based on the comprehensive scale deposit quantity and the historical scale deposit data, it includes: The scale deposit similarity is obtained from the following formula: ； Where S represents the scale deposit similarity, G1 represents the comprehensive scale deposit quantity, G2 represents the maximum value of the historical comprehensive scale deposit quantity, and G3 represents the mean value of the historical comprehensive scale deposit quantity.

7. The energy consumption evaluation method for a heat pump system according to claim 6, wherein When the scale deposit similarity is greater than or equal to the scale deposit similarity threshold, determine the predicted scale deposit quantity according to the historical scale deposit data. When the scale deposit similarity is less than the scale deposit similarity threshold, divide the historical scale deposit data into a scale deposit correction set, and determine the predicted scale deposit quantity according to the scale deposit correction set. It includes: Preset the scale deposit similarity threshold in advance; When the scale deposit similarity is greater than or equal to the scale deposit similarity threshold, determine the maximum value of the historical comprehensive scale deposit quantity as the predicted scale deposit quantity; The scale deposit correction set includes a first scale deposit correction set, a second scale deposit correction set, and a third scale deposit correction set; When the scale deposit similarity is less than the scale deposit similarity threshold, divide the historical comprehensive scale deposit quantities greater than the mean value of the historical comprehensive scale deposit quantity into the first scale deposit correction set, divide the historical comprehensive scale deposit quantities equal to the mean value of the historical comprehensive scale deposit quantity into the second scale deposit correction set, and divide the historical comprehensive scale deposit quantities less than the mean value of the historical comprehensive scale deposit quantity into the third scale deposit correction set; Determine the predicted scale deposit quantity according to the first scale deposit correction set, the second scale deposit correction set, and the third scale deposit correction set.

8. The energy consumption evaluation method for a heat pump system according to claim 7, characterized in that, When determining the predicted scale deposit quantity according to the first scale deposit correction set, the second scale deposit correction set, and the third scale deposit correction set, it includes: The predicted scale deposit quantity is obtained from the following formula: ； Wherein, K represents the predicted scale quantity, G2 represents the maximum value of the comprehensive historical scale quantity, Y1 represents the maximum value of the comprehensive historical scale quantity in the first scale correction set, Y2 represents the minimum value of the comprehensive historical scale quantity in the first scale correction set, U1 represents the maximum value of the comprehensive historical scale quantity in the second scale correction set, U2 represents the minimum value of the comprehensive historical scale quantity in the second scale correction set, I1 represents the maximum value of the comprehensive historical scale quantity in the third scale correction set, and I2 represents the minimum value of the comprehensive historical scale quantity in the third scale correction set.

9. The energy consumption evaluation method for a heat pump system according to claim 8, characterized in that When setting the energy consumption correction coefficient of the predicted energy consumption value according to the predicted scale quantity and determining the target predicted energy consumption value according to the energy consumption correction coefficient and the predicted energy consumption value, it includes: Presetting a first preset predicted scale quantity and a second preset predicted scale quantity, where the first preset predicted scale quantity is greater than the second preset predicted scale quantity; Presetting a first preset energy consumption correction coefficient, a second preset energy consumption correction coefficient, and a third preset energy consumption correction coefficient, where the first preset energy consumption correction coefficient is greater than the second preset energy consumption correction coefficient, and the second preset energy consumption correction coefficient is greater than the third preset energy consumption correction coefficient; When the predicted scale quantity is greater than the first preset predicted scale quantity, the first preset energy consumption correction coefficient is used as the energy consumption correction coefficient of the predicted energy consumption value; When the predicted scale quantity is less than or equal to the first preset predicted scale quantity and greater than the second preset predicted scale quantity, the second preset energy consumption correction coefficient is used as the energy consumption correction coefficient of the predicted energy consumption value; When the predicted scale quantity is less than or equal to the second preset predicted scale quantity, the third preset energy consumption correction coefficient is used as the energy consumption correction coefficient of the predicted energy consumption value; The target predicted energy consumption value is the product value of the predicted energy consumption value and the energy consumption correction coefficient.

10. An energy consumption evaluation system for a heat pump system, which is applied to the energy consumption evaluation method for a heat pump system according to any one of claims 1-9, characterized in that, It includes: An energy consumption acquisition module, configured to acquire the output temperature data of the heat pump system and the indoor temperature data of the residential area, determine the current energy consumption value according to the output temperature data and the indoor temperature data, acquire the historical output temperature data and historical indoor temperature data, and determine the predicted energy consumption value based on the random forest model; An energy consumption analysis module, configured to divide the evaporator into several scale detection areas, acquire the scale images of each scale detection area, analyze each scale image to determine the comprehensive scale quantity of the evaporator, compare the comprehensive scale quantity with the historical scale data, judge whether the comprehensive scale quantity is correct according to the comparison result, and when it is determined that the comprehensive scale quantity is incorrect, determine the scale similarity based on the comprehensive scale quantity and the historical scale data; An energy consumption processing module, configured to determine the predicted scale quantity according to the historical scale data when the scale similarity is greater than or equal to the scale similarity threshold, and when the scale similarity is less than the scale similarity threshold, divide the historical scale data to determine the scale correction set, and determine the predicted scale quantity according to the scale correction set; An energy consumption determination module, configured to set an energy consumption correction coefficient of the predicted energy consumption value according to the predicted scale quantity, and determine a target predicted energy consumption value according to the energy consumption correction coefficient and the predicted energy consumption value.

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