Intelligent campus energy management optimization operation method

Abstract

The invention provides a smart campus energy management optimization operation method. The method comprises the steps of obtaining and processing historical energy operation data and historical electric power energy consumption data to obtain an energy consumption demand model, obtaining real-time electric power demand prediction data according to energy operation demand data processing, obtaining real-time environment parameter data, and comparing the real-time environment parameter data with reference data to obtain a real-time environment deviation rate; the method comprises the following steps of: obtaining an electric power demand influence factor by combining with a temperature regulation illumination area change rate and a real-time energy supply power fluctuation rate, optimizing real-time electric power demand prediction data to obtain real-time electric power demand prediction optimization data, performing aggregation processing to obtain total electric power demand prediction data, and finally performing comparison processing on the total electric power demand prediction data and self-power supply rated data to obtain the self-power supply rated data. Judging a self-energized operation state; according to the invention, the energy consumption demand model is trained, the real-time power demand prediction data is obtained and optimized, then the data is compared with the self-powered rated data, and self-powered optimization adjustment is carried out on the short-short and insufficient state, so that intelligent management and optimization of campus energy are realized.

Description

Technical Field

[0001] The present application relates to the field of campus energy management technology, and more specifically, to a method for optimizing operation of smart campus energy management. Background Art

[0002] With the continuous expansion of campus scale and the increasing number of modern facilities, campus energy consumption shows a rapid growth trend. The traditional campus energy management method is relatively extensive and lacks refined monitoring, control and optimization methods, resulting in serious energy waste and low energy utilization efficiency. At the same time, it is difficult to intelligently manage and coordinate the various energy equipment on campus, and it is impossible to flexibly adjust the energy supply strategy according to actual needs, which affects the stability and reliability of the campus energy system. Therefore, it is necessary to develop an energy management optimization operation method to improve the level of campus energy management, reduce energy consumption, and achieve sustainable development of the campus.

[0003] In view of the above problems, effective technical solutions are urgently needed. Summary of the invention

[0004] The purpose of this application is to provide a smart campus energy management optimization operation method. By training the energy consumption demand model, real-time power demand forecast data can be obtained and optimized, and then compared with the self-powered rated data, and the self-powered optimization adjustment can be made for the short-term loss state, thereby realizing the intelligent management and optimization of campus energy.

[0005] This application also provides a smart campus energy management optimization operation method, including the following steps: Obtain historical energy operation data and corresponding historical power consumption data within a preset campus range within multiple historical time periods, and process the historical energy operation data and the corresponding historical power consumption data to obtain an energy consumption demand model; Obtaining energy operation demand data within a preset time period within the preset campus range, processing the energy operation demand data, and obtaining corresponding real-time power demand forecast data; Obtain real-time weather information within the preset campus range and extract real-time environmental parameter data, compare with the benchmark data to obtain the real-time environmental deviation rate, and then process it in combination with the temperature control lighting area change rate and the real-time energy supply power fluctuation rate to obtain the power demand influencing factor; The real-time power demand forecast data is processed in combination with the power demand influencing factors to obtain real-time power demand forecast optimization data, and the total power demand forecast data of the smart campus is obtained by aggregation processing; The self-power supply rated data of the smart campus is compared with the total power demand forecast data to determine the self-power supply operation status, and optimize the self-power supply for the short-term and deficit status.

[0006] Optionally, in the smart campus energy management optimization operation method described in the present application, the obtaining of historical energy operation data and corresponding historical power consumption data within a preset campus range within multiple historical time periods, and processing the historical energy operation data and the corresponding historical power consumption data to obtain an energy consumption demand model include: Obtain multiple historical energy operation data and corresponding multiple historical power consumption data within multiple historical time periods within a preset campus range; The historical energy operation data includes historical temperature adjustment data, historical temperature adjustment duration data, historical temperature adjustment area data, historical light brightness data, historical illumination area data and historical illumination duration data; The initial energy consumption demand model is trained based on multiple historical temperature control data, historical temperature control duration data, historical temperature control area data, historical light brightness data, historical illumination area data and historical illumination duration data combined with multiple corresponding historical power consumption data to obtain a trained energy consumption demand model.

[0007] Optionally, in the smart campus energy management optimization operation method described in the present application, the step of obtaining energy operation demand data within a preset time period within the preset campus range, and processing the energy operation demand data to obtain corresponding real-time power demand forecast data includes: Obtain energy operation demand data within a preset campus range within a preset time period, including temperature control demand data, temperature control duration preset data, temperature control area data, light brightness demand data, illumination area data, and illumination duration preset data; The temperature control demand data, temperature control time preset data, temperature control area data, light brightness demand data, illumination area data and illumination time preset data are input into the trained energy consumption demand model for processing to obtain real-time power demand forecast data.

[0008] Optionally, in the smart campus energy management optimization operation method described in the present application, the real-time weather information of the preset campus range is obtained and the real-time environmental parameter data is extracted, and the real-time environmental deviation rate is obtained by comparison with the benchmark data, and then combined with the temperature control lighting area change rate and the real-time energy supply power fluctuation rate for processing to obtain the power demand influencing factor, including: Obtain real-time weather information within the preset campus range, and extract real-time environmental parameter data based on the real-time weather information, including real-time temperature data, real-time humidity data, and real-time brightness data; The real-time temperature data, the real-time humidity data and the real-time brightness data are respectively compared with the preset environmental reference temperature data, the environmental reference humidity data and the environmental reference brightness data to obtain the real-time environmental deviation rate, including the environmental temperature deviation rate, the environmental humidity deviation rate and the environmental brightness deviation rate; Obtain the temperature control lighting area change rate within the preset campus range, and obtain the real-time power supply fluctuation rate of the campus energy system; The ambient temperature deviation rate, ambient humidity deviation rate and ambient brightness deviation rate as well as the temperature-adjusting lighting area change rate and the real-time energy supply power fluctuation rate are input into a preset power demand impact assessment model for processing to obtain corresponding power demand impact factors.

[0009] Optionally, in the smart campus energy management optimization operation method described in the present application, the real-time power demand forecast data is processed in combination with the power demand influencing factor to obtain real-time power demand forecast optimization data, and the total power demand forecast data of the smart campus is obtained by aggregation processing, including: Processing the real-time power demand forecast data in combination with the power demand influencing factors to obtain real-time power demand forecast optimization data corresponding to a preset campus range; The real-time power demand forecast optimization data corresponding to multiple preset campus ranges are aggregated to obtain the total power demand forecast data corresponding to the smart campus.

[0010] Optionally, in the smart campus energy management optimization operation method described in the present application, the self-power supply rated data of the smart campus is compared with the total power demand forecast data to determine the self-power supply operation state, and the self-power supply optimization adjustment is performed on the short-term deficit state, including: Obtain the self-power rating data of the smart campus; Comparing the self-powered rated data with the total power demand forecast data to obtain a power demand deviation rate; Performing a threshold comparison between the electric energy demand deviation rate and a preset electric energy demand deviation rate threshold; If it is less than or equal to the preset power demand deviation rate threshold, the self-power supply operation state is the self-power supply normal state; If it is greater than the preset power demand deviation rate threshold, the self-energy supply operation state is a self-energy shortfall state, and the self-energy supply operation plan is optimized and adjusted.

[0011] Optionally, in the smart campus energy management optimization operation method described in the present application, the self-powered rated data is compared with the total power demand forecast data to obtain the power demand deviation rate, including: Comparing the self-powered rated data with the total power demand forecast data; If the self-powered rated data is greater than or equal to the total power demand forecast data, the self-powered operation state is the self-powered normal state; If the self-powered rated data is less than the total power demand forecast data, the power supply capacity rated data and the total power demand forecast data are processed to obtain the power demand deviation rate.

[0012] Optionally, the smart campus energy management optimization operation method described in the present application further includes: Obtain water supply operation data within a preset campus range, process the water supply operation data, and obtain a water supply imbalance evaluation index; Comparing the water supply imbalance evaluation index with a preset water supply balance benchmark index to obtain a relative value of water supply balance; The relative value of the water supply balance is compared with the preset water supply balance threshold: If it is less than or equal to the preset water supply balance threshold, the water supply operation is judged to be normal; If it is greater than the preset water supply balance threshold, the water supply operation is judged to be abnormal, and the early warning response and water supply imbalance location information are output to the management end for display.

[0013] Optionally, in the smart campus energy management optimization operation method described in the present application, the step of obtaining water supply operation data within a preset campus range, and processing the water supply operation data to obtain a water supply imbalance evaluation index includes: Obtain water supply operation data within a preset campus range, including water pressure difference and water flow difference; The water pressure difference and the water flow difference are input into a preset water supply balance evaluation model for processing to obtain a water supply imbalance evaluation index.

[0014] Optionally, the smart campus energy management optimization operation method described in the present application further includes: Obtain the distance data between people and each light source within the preset campus range; Comparing the plurality of distance data with preset reference distance data respectively; If it is greater than the preset reference distance data, the light source shutdown response is activated, and the average brightness of the preset range of personnel is obtained; Comparing the brightness mean with the preset required brightness; If it is less than the preset standard brightness, the light source with the smallest distance data is activated to respond.

[0015] From the above, it can be seen that the smart campus energy management optimization operation method provided in this application obtains real-time electricity demand forecast data and optimizes the processing by training the energy consumption demand model, then compares it with the self-powered rated data, and optimizes the self-powered adjustment for the short-term loss state, thereby realizing the intelligent management and optimization of campus energy.

[0016] Other features and advantages of the present application will be described in the following description, and partly become apparent from the description, or understood by practicing the embodiments of the present application. The purpose and other advantages of the present application can be realized and obtained by the structures specifically pointed out in the written description and the drawings. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings required for use in the embodiments of the present application will be briefly introduced below. It should be understood that the following drawings only show certain embodiments of the present application and therefore should not be regarded as limiting the scope. For ordinary technicians in this field, other related drawings can be obtained based on these drawings without paying creative work.

[0018] Figure 1 A flowchart of the smart campus energy management optimization operation method provided in the embodiment of the present application; Figure 2 A flow chart of obtaining an energy consumption demand model for the smart campus energy management optimization operation method provided in an embodiment of the present application; Figure 3 A flowchart of obtaining real-time power demand forecast data for the smart campus energy management optimization operation method provided in an embodiment of the present application; Figure 4 A flow chart of obtaining power demand influencing factors of the smart campus energy management optimization operation method provided in an embodiment of the present application; Figure 5 A flowchart of obtaining total power demand forecast data for a smart campus in accordance with the smart campus energy management optimization operation method provided in an embodiment of the present application. DETAILED DESCRIPTION

[0019] The technical solutions in the embodiments of the present application will be clearly and completely described below in conjunction with the drawings in the embodiments of the present application. Obviously, the described embodiments are only a part of the embodiments of the present application, rather than all of the embodiments. The components of the embodiments of the present application described and shown in the drawings here can be arranged and designed in various different configurations. Therefore, the following detailed description of the embodiments of the present application provided in the drawings is not intended to limit the scope of the application claimed for protection, but merely represents the selected embodiments of the present application. Based on the embodiments of the present application, all other embodiments obtained by those skilled in the art without making creative work belong to the scope of protection of the present application.

[0020] It should be noted that similar reference numerals and letters represent similar items in the following drawings, so once an item is defined in one drawing, it does not need to be further defined and explained in subsequent drawings. At the same time, in the description of this application, the terms "first", "second", etc. are only used to distinguish the description and cannot be understood as indicating or implying relative importance.

[0021] Please refer to Figure 1 , Figure 1 Flow chart of the smart campus energy management optimization operation method in some embodiments of the present application. The smart campus energy management optimization operation method is used in terminal devices, such as computers, mobile terminals, etc. The smart campus energy management optimization operation method includes the following steps: S11, obtaining historical energy operation data and corresponding historical power consumption data within a preset campus range within multiple historical time periods, and processing the historical energy operation data and the corresponding historical power consumption data to obtain an energy consumption demand model; S12, obtaining energy operation demand data within a preset time period within the preset campus range, processing the energy operation demand data, and obtaining corresponding real-time power demand forecast data; S13, obtaining real-time weather information within a preset campus range and extracting real-time environmental parameter data, comparing it with the benchmark data to obtain the real-time environmental deviation rate, and then processing it in combination with the temperature control lighting area change rate and the real-time energy supply power fluctuation rate to obtain the power demand influencing factor; S14, processing the real-time power demand forecast data in combination with the power demand influencing factors to obtain real-time power demand forecast optimization data, and performing aggregation processing to obtain total power demand forecast data for the smart campus; S15. Compare and process the self-power supply rated data of the smart campus with the total power demand forecast data to determine the self-power supply operation status and optimize the self-power supply for the short-term and deficit status.

[0022] It should be noted that in order to manage and optimize campus energy according to demand, first obtain the historical energy operation data and the corresponding historical power consumption data of the preset campus scope in multiple historical time periods. The preset campus scope is a classroom, an office or a conference room and other spaces. Use multiple historical energy operation data and corresponding multiple historical power consumption data as data sets for model training to obtain an energy consumption demand model. Then obtain the energy operation demand data of the preset campus scope in the preset time period and process it to obtain the corresponding real-time power demand forecast data. At the same time, the power demand situation will be affected by many factors, such as real-time weather, preset campus area, power consumption equipment, etc. Therefore, we continue to obtain real-time weather information within the preset campus range and extract real-time environmental parameter data, compare it with the benchmark data to obtain the real-time environmental deviation rate, and then process it in combination with the temperature control lighting area change rate and the real-time energy supply power fluctuation rate to obtain the power demand influencing factor, and further optimize the real-time power demand forecast data to obtain the real-time power demand forecast optimization data, and aggregate it to obtain the total power demand forecast data of the smart campus. Finally, we compare the self-powered rated data of the smart campus with the total power demand forecast data to judge the self-powered operation status, and make self-powered optimization adjustments to the short-term deficit status, such as adjusting the self-powered equipment parameters and activating the State Grid power supply response.

[0023] Please refer to Figure 2 , Figure 2 It is a flow chart of obtaining an energy consumption demand model of a smart campus energy management optimization operation method in some embodiments of the present application. According to an embodiment of the present invention, the acquisition of historical energy operation data and corresponding historical power consumption data within a preset campus range within multiple historical time periods, and processing according to the historical energy operation data and the corresponding historical power consumption data to obtain an energy consumption demand model include: S21, obtaining a plurality of historical energy operation data and corresponding plurality of historical power consumption data within a plurality of historical time periods within a preset campus range; S22, the historical energy operation data includes historical temperature adjustment data, historical temperature adjustment duration data, historical temperature adjustment area data, historical light brightness data, historical illumination area data and historical illumination duration data; S23. Train the initial energy consumption demand model based on the multiple historical temperature control data, historical temperature control duration data, historical temperature control area data, historical light brightness data, historical illumination area data and historical illumination duration data in combination with multiple corresponding historical power consumption data to obtain a trained energy consumption demand model.

[0024] It should be noted that in order to predict the real-time electricity demand, it is first necessary to analyze the historical data and train the corresponding energy consumption demand model, by obtaining multiple historical energy operation data and corresponding multiple historical electricity consumption data within a preset campus range in multiple historical time periods, where the historical energy operation data includes historical temperature control data, historical temperature control duration data, historical temperature control area area data, historical light brightness data, historical illumination area data and historical illumination duration data, and the data is pre-processed and divided into a training set, a verification set and a test set; the initial energy consumption demand model is trained according to multiple historical temperature control data, historical temperature control duration data, historical temperature control area area data, historical light brightness data, historical illumination area data and historical illumination duration data combined with multiple corresponding historical electricity consumption data to obtain a trained energy consumption demand model for predicting and analyzing the electricity demand situation according to the energy operation demand data obtained in real time.

[0025] Please refer to Figure 3 , Figure 3 The flowchart of the method for optimizing the operation of energy management in a smart campus in some embodiments of the present application is to obtain real-time power demand forecast data. According to an embodiment of the present invention, the energy operation demand data of the preset campus range within a preset time period is obtained, and the corresponding real-time power demand forecast data is obtained according to the energy operation demand data, including: S31, obtaining energy operation demand data within a preset time period within a preset campus range, including temperature adjustment demand data, temperature adjustment time preset data, temperature adjustment area data, light brightness demand data, illumination area data, and illumination time preset data; S32, input the temperature control demand data, temperature control time preset data, temperature control area data, light brightness demand data, illumination area data and illumination time preset data into the trained energy consumption demand model for processing to obtain real-time power demand forecast data.

[0026] It should be noted that in order to accurately understand the real-time power demand situation, first, obtain the energy operation demand data of the preset campus range within the preset time period, including temperature control demand data, temperature control time preset data, temperature control area area data, light brightness demand data, illumination area data and illumination time preset data, and input the temperature control demand data, temperature control time preset data, temperature control area area data, light brightness demand data, illumination area data and illumination time preset data into the trained energy consumption demand model for processing to obtain real-time power demand forecast data; The calculation formula for real-time power demand forecast data in the energy consumption demand model is: ; in, For real-time electricity demand forecast data, , , , , , They are temperature control demand data, temperature control duration preset data, temperature control area data, light brightness demand data, illumination area data and illumination duration preset data. , , It is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset smart campus energy management optimization operation platform).

[0027] Please refer to Figure 4 , Figure 4 It is a flow chart of obtaining the power demand influencing factor of the smart campus energy management optimization operation method in some embodiments of the present application. According to the embodiment of the present invention, the real-time weather information of the preset campus range is obtained and the real-time environmental parameter data is extracted, and the real-time environmental deviation rate is obtained by comparing with the benchmark data, and then combined with the temperature control lighting area change rate and the real-time energy supply power fluctuation rate for processing to obtain the power demand influencing factor, including: S41, obtaining real-time weather information of a preset campus range, and extracting real-time environmental parameter data according to the real-time weather information, including real-time temperature data, real-time humidity data, and real-time brightness data; S42, comparing the real-time temperature data, real-time humidity data and real-time brightness data with preset environmental reference temperature data, environmental reference humidity data and environmental reference brightness data respectively, to obtain real-time environmental deviation rate, including environmental temperature deviation rate, environmental humidity deviation rate and environmental brightness deviation rate; S43, obtaining the temperature control lighting area change rate of the preset campus range, and obtaining the real-time energy supply power fluctuation rate of the campus energy system; S44, input the ambient temperature deviation rate, ambient humidity deviation rate, ambient brightness deviation rate, temperature control lighting area change rate, and real-time energy supply power fluctuation rate into a preset power demand impact assessment model for processing, and obtain corresponding power demand impact factors.

[0028] It should be noted that during the operation of the campus energy system, the power demand will be affected by many factors. Therefore, it is necessary to reasonably correct the obtained real-time power demand forecast data according to these factors. In this embodiment, the real-time weather, temperature-controlled lighting area and energy supply power changes are mainly studied. First, the real-time weather information of the preset campus range is obtained, and the real-time environmental parameter data including real-time temperature data, real-time humidity data and real-time brightness data are extracted according to the real-time weather information. The data are compared with the preset environmental benchmark temperature data, environmental benchmark humidity data and environmental benchmark brightness data respectively to obtain the real-time environmental deviation rate including the environmental temperature deviation rate, environmental humidity deviation rate and environmental brightness deviation rate, and then the temperature-controlled lighting area change rate of the preset campus range and the real-time energy supply power fluctuation rate of the campus energy system are obtained. The environmental temperature deviation rate, environmental humidity deviation rate and environmental brightness deviation rate as well as the temperature-controlled lighting area change rate and the real-time energy supply power fluctuation rate are input into the preset power demand impact evaluation model for processing to obtain the corresponding power demand impact factor; The calculation formula of the power demand impact factor in the power demand impact assessment model is: ; in, is the factor affecting electricity demand, , , They are the ambient temperature deviation rate, ambient humidity deviation rate and ambient brightness deviation rate, , Temperature control lighting area change rate and real-time energy supply power fluctuation rate, , It is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset smart campus energy management optimization operation platform).

[0029] Please refer to Figure 5 , Figure 5 The present invention is a flowchart of a smart campus energy management optimization operation method in some embodiments of the present invention for obtaining the total power demand forecast data of the smart campus. According to an embodiment of the present invention, the real-time power demand forecast data is processed in combination with the power demand influencing factor to obtain the real-time power demand forecast optimization data, and the total power demand forecast data of the smart campus is obtained by aggregation processing, including: S51, processing the real-time power demand forecast data in combination with the power demand influencing factors to obtain real-time power demand forecast optimization data corresponding to a preset campus range; S52. Aggregate the real-time power demand forecast optimization data corresponding to multiple preset campus ranges to obtain the total power demand forecast data corresponding to the smart campus.

[0030] It should be noted that, in order to make the obtained real-time power demand forecast data more consistent with the current operating status, the real-time power demand forecast data is processed according to the obtained power demand influencing factors to obtain corresponding real-time power demand forecast optimization data; The real-time power demand forecast optimization data calculation formula is: ; in, Optimizing data for real-time electricity demand forecasting, , They are the power demand influencing factors and real-time power demand forecast data, is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset smart campus energy management optimization operation platform); Since there are multiple preset campus ranges, the real-time power demand forecast optimization data corresponding to the preset campus ranges are aggregated to obtain the total power demand forecast data corresponding to the smart campus. For example, there are 5 preset campus ranges, and the real-time power demand forecast optimization data obtained are 7, 9, 10, 5, and 20, respectively. Then 7+9+10+5+20=51 is the total power demand forecast data corresponding to the smart campus.

[0031] According to an embodiment of the present invention, the self-power supply rated data of the smart campus is compared with the total power demand forecast data to determine the self-power supply operation state, and the self-power supply optimization adjustment is performed on the short-term deficit state, including: Obtain the self-power rating data of the smart campus; Comparing the self-powered rated data with the total power demand forecast data to obtain a power demand deviation rate; Performing a threshold comparison between the electric energy demand deviation rate and a preset electric energy demand deviation rate threshold; If it is less than or equal to the preset power demand deviation rate threshold, the self-power supply operation state is the self-power supply normal state; If it is greater than the preset power demand deviation rate threshold, the self-energy supply operation state is a self-energy shortfall state, and the self-energy supply operation plan is optimized and adjusted.

[0032] It should be noted that in order to judge the self-powered operation status of the smart campus, the self-powered rated data of the smart campus is first obtained, and then the obtained total power demand forecast data is compared with the self-powered rated data to obtain the power demand deviation rate. Finally, the obtained power demand deviation rate is compared with the preset power demand deviation rate threshold. In this embodiment, the power demand deviation rate threshold is set to (0, 0.1], (0.1, 1], corresponding to the normal state of self-powered and the short-term state of self-powered, respectively. For example, if the obtained power demand deviation rate is 0.1, it corresponds to the normal state of self-powered. If the obtained power demand deviation rate is 0.2, the self-powered operation state is the self-powered short-term state, indicating that the self-powered is insufficient, and the self-powered operation plan needs to be optimized and adjusted, such as adjusting the parameters of the self-powered equipment or activating the state grid power supply response.

[0033] According to an embodiment of the present invention, the step of comparing the self-powered rated data with the total power demand forecast data to obtain the power demand deviation rate includes: Comparing the self-powered rated data with the total power demand forecast data; If the self-powered rated data is greater than or equal to the total power demand forecast data, the self-powered operation state is the self-powered normal state; If the self-powered rated data is less than the total power demand forecast data, the power supply capacity rated data and the total power demand forecast data are processed to obtain the power demand deviation rate.

[0034] It should be noted that in order to obtain the deviation rate of electric energy demand, the total power demand forecast data obtained is first compared with the self-powered rated data. If the self-powered rated data is greater than or equal to the total power demand forecast data, it means that the self-power supply capacity of the smart campus is sufficient and there is no need to further calculate the deviation rate. If the self-powered rated data is less than the total power demand forecast data, it is necessary to further judge the deviation and process it according to the power supply capacity rated data and the total power demand forecast data to obtain the electric energy demand deviation rate. The calculation formula of the electric energy demand deviation rate is: ; in, is the power demand deviation rate, , They are self-power supply rated data and total power demand forecast data respectively.

[0035] According to an embodiment of the present invention, it also includes: Obtain water supply operation data within a preset campus range, process the water supply operation data, and obtain a water supply imbalance evaluation index; Comparing the water supply imbalance evaluation index with a preset water supply balance benchmark index to obtain a relative value of water supply balance; The relative value of the water supply balance is compared with the preset water supply balance threshold: If it is less than or equal to the preset water supply balance threshold, the water supply operation is judged to be normal; If it is greater than the preset water supply balance threshold, the water supply operation is judged to be abnormal, and the early warning response and water supply imbalance location information are output to the management end for display.

[0036] It should be noted that, on campus, water supply energy management is an important part of ensuring the normal operation of the campus. In order to judge the water supply operation status on campus, firstly, the water supply operation data of the preset campus range is obtained and processed to obtain the water supply imbalance evaluation index, and then compared with the preset water supply balance benchmark index to obtain the relative value of water supply balance. For example, the obtained water supply imbalance evaluation index is 8, and the preset water supply balance benchmark index is 10, then 8 / 10=0.8 is the relative value of water supply balance; finally, the obtained relative value of water supply balance is compared with the preset water supply balance threshold. In this embodiment, the water supply balance threshold is set to (0, 0.7], (0.7, 1], corresponding to normal water supply operation and abnormal water supply operation, respectively. For example, if the obtained relative value of water supply balance is 0.5, it is determined that the water supply operation is normal. If the obtained relative value of water supply balance is 0.8, it is determined that the water supply operation is abnormal, and the warning response and water supply imbalance location information are output to the management end for display.

[0037] According to an embodiment of the present invention, the step of obtaining water supply operation data within a preset campus range and processing the water supply operation data to obtain a water supply imbalance evaluation index includes: Obtain water supply operation data within a preset campus range, including water pressure difference and water flow difference; The water pressure difference and the water flow difference are input into a preset water supply balance evaluation model for processing to obtain a water supply imbalance evaluation index.

[0038] It should be noted that in order to obtain the water supply imbalance evaluation index, firstly, the water supply operation data including the water pressure difference and the water flow difference within the preset campus range is obtained, and the water pressure difference and the water flow difference are input into the preset water supply balance evaluation model for processing to obtain the water supply imbalance evaluation index; The calculation formula of the water supply imbalance evaluation index in the water supply balance evaluation model is: ; in, is the water supply imbalance evaluation index, , are the water pressure difference and water flow difference respectively, , It is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset smart campus energy management optimization operation platform).

[0039] According to an embodiment of the present invention, it also includes: Obtain the distance data between people and each light source within the preset campus range; Comparing the plurality of distance data with preset reference distance data respectively; If it is greater than the preset reference distance data, the light source shutdown response is activated, and the average brightness of the preset range of personnel is obtained; Comparing the brightness mean with the preset required brightness; If it is less than the preset standard brightness, the light source with the smallest distance data is activated to respond.

[0040] It should be noted that within the preset range of the campus, the problem of excessive light source opening often occurs. In order to realize intelligent control of the light source, the distance data between the people and each light source within the preset campus range is first obtained, and then the distance data is compared with the preset benchmark distance data. If it is less than or equal to the preset benchmark distance data, the light source remains on. If it is greater than the preset benchmark distance data, it means that the light source is far away from the person, then the light source is activated to turn off the response, and the average brightness of the preset range of the person is obtained. Finally, the average brightness is compared with the preset required brightness. If it is greater than or equal to the preset required brightness, it means that the brightness at the person's location can meet the demand. If it is less than the preset required brightness, it means that the brightness at the person's location cannot meet the demand, then the light source with the smallest distance data is activated to turn on the response, increase the brightness, and ensure the comfort of the environment.

[0041] It is worth mentioning that according to an embodiment of the present invention, if the power demand deviation rate is greater than the preset power demand deviation rate threshold, the self-power supply operation state is a self-power supply shortfall state, and the self-power supply operation scheme is optimized and adjusted, including: If it is greater than the preset power demand deviation rate threshold, the self-power supply operation state is a self-power supply shortfall state; Comparing the electric energy demand deviation rate with a preset reference deviation rate by threshold value to obtain a relative value of the electric energy demand deviation rate; Performing a threshold comparison between the relative value of the electric energy demand deviation rate and a preset reference deviation rate threshold; If it is less than or equal to the preset reference deviation rate threshold, the self-powered device parameters are adjusted; If it is greater than the preset benchmark deviation rate threshold, the self-powered equipment parameters are adjusted and the State Grid power supply response is activated.

[0042] It should be noted that, in order to clarify how to optimize the energy supply adjustment when the self-energy supply operation state is a self-energy supply shortfall state, the obtained electric energy demand deviation rate is compared with the preset benchmark deviation rate by threshold value to obtain the relative value of the electric energy demand deviation rate. For example, the obtained electric energy demand deviation rate is 0.1, and the preset benchmark deviation rate is 0.5, then 0.1 / 0.5=0.2 is the relative value of the electric energy demand deviation rate, and the obtained relative value of the electric energy demand deviation rate is compared with the preset benchmark deviation rate threshold value. In this embodiment, the benchmark deviation rate threshold is set to (0, 0.2], (0.2, 1], which respectively correspond to adjusting the self-energy supply equipment parameters, adjusting the self-energy supply equipment parameters and activating the state grid power supply response. For example, if the relative value of the electric energy demand deviation rate is 0.1, then only the self-energy supply equipment parameters need to be adjusted to improve the power supply capacity. If the relative value of the electric energy demand deviation rate is 0.3, it is necessary to adjust the self-energy supply equipment parameters and activate the state grid power supply response.

[0043] It is worth mentioning that according to an embodiment of the present invention, it also includes: Get the working time interval and non-working time interval corresponding to the preset campus range; Obtain the detection time point data and corresponding real-time power data within the preset campus range; If the detection time point data is within the working time interval, the real-time power data is compared with the preset first standard power data; If it is greater than or equal to the preset first standard power data, a first warning response is output; If it is less than the preset first standard power data, it is determined to be a normal energy consumption state; If the detection time point data is within the non-working time interval, the real-time power data is compared with the preset second standard power data; If it is greater than or equal to the preset second standard power data, a second warning response is output; If it is less than the preset second standard power data, it is determined to be a normal energy consumption state.

[0044] It should be noted that the time distribution of the operation of energy-consuming equipment on campus is relatively concentrated, mainly divided into working hours and non-working hours. For example, 08:00-12:00 is working hours, and 12:01-13:30 is non-working hours. If the energy-consuming equipment is not shut down in time or has malfunctions, it will cause energy waste. In order to realize intelligent monitoring of abnormal energy consumption during working hours and non-working hours, first obtain the working time interval and non-working time interval corresponding to the preset campus range, and then obtain the detection time point data and corresponding real-time power data of the preset campus range. If the detection time point data is within the working time interval, the real-time power data is compared with the preset first standard. The power data is compared. If it is greater than or equal to the preset first standard power data, it means that abnormal energy consumption equipment is in operation or the energy consumption equipment has an abnormality, then the first early warning response is output to remind the operation and maintenance personnel to check. If it is less than the preset first standard power data, it is determined to be a normal energy consumption state. If the data at the detection time point is within the non-working time interval, the real-time power data is compared with the preset second standard power data. If it is greater than or equal to the preset second standard power data, it means that some energy consumption equipment has not been shut down in time, then the second early warning response is output to remind the operation and maintenance personnel to check and shut down in time. If it is less than the preset second standard power data, it is determined to be a normal energy consumption state.

[0045] It is worth mentioning that according to an embodiment of the present invention, it also includes: Obtain room temperature data and personnel density data within the radiation range of the air-conditioning equipment within a preset campus range, and process the room temperature data and the temperature adjustment demand data to obtain a temperature control difference; Inputting the temperature control difference and personnel density data into a preset temperature control demand index evaluation model for processing to obtain a temperature control demand index; A preset air conditioning equipment parameter list is queried according to the temperature control demand index to obtain the air conditioning equipment setting parameters.

[0046] It should be noted that when there are fewer people within the preset range of the campus, if all air-conditioning equipment is turned on, it will lead to excessive energy consumption. In order to realize intelligent control of the air-conditioning equipment, firstly, the room temperature data and the personnel density data of the radiation range of the air-conditioning equipment within the preset campus range are obtained, wherein the personnel density data refers to the ratio of the total number of people within the radiation range of the air-conditioning equipment to the radiation area of ​​the air-conditioning equipment, and the room temperature data and the temperature control demand data are processed to obtain the temperature control difference, which refers to the absolute value of the difference between the room temperature data and the temperature control demand data, and then the temperature control difference and the personnel density data are input into the preset temperature control demand index evaluation model for processing to obtain the temperature control demand index; The temperature control demand index calculation formula in the temperature control demand index evaluation model is: ; in, is the temperature control demand index, , They are temperature control difference and personnel density data respectively. is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset smart campus energy management optimization operation platform); The preset air-conditioning equipment parameter list is queried according to the obtained temperature control demand index, wherein the air-conditioning equipment parameter list is obtained by querying the preset smart campus energy management optimization operation platform, and the operation of the air-conditioning equipment is controlled according to the queried air-conditioning equipment setting parameters to ensure environmental comfort while reducing energy consumption.

[0047] It is worth mentioning that according to an embodiment of the present invention, it also includes: Obtain historical per capita water demand data within a preset campus range and a preset time period; Acquire real-time total number of personnel data and real-time water supply capacity data within a preset time period within a preset campus range, and process the real-time total number of personnel data and the historical per capita water demand data to obtain water demand forecast data; Comparing the water demand forecast data with the real-time water supply capacity data to obtain a real-time water supply capacity deviation rate; Comparing the real-time water supply capacity deviation rate with a preset water supply capacity deviation rate threshold; If it is less than or equal to the preset water supply capacity deviation rate threshold, the water supply capacity state is determined to be a normal water supply state; If it is greater than the preset water supply capacity deviation rate threshold, the water supply capacity status is determined to be an abnormal water supply status, and the water pump speed setting parameters are adjusted.

[0048] It should be noted that in the daily operation of the campus, in order to understand the water supply capacity and realize the intelligent management of water supply energy, first obtain the historical per capita water demand data within the preset campus range and preset time period, then obtain the corresponding real-time total number of personnel data and real-time water supply capacity data, and process the real-time total number of personnel data and historical per capita water demand data to obtain water demand forecast data; The water demand forecast data calculation formula is: ; in, For water demand forecast data, , They are real-time total population data and historical per capita water demand data. is the preset characteristic coefficient (the characteristic coefficient is obtained by querying the preset smart campus energy management optimization operation platform); The obtained water demand forecast data is compared with the real-time water supply capacity data to obtain the real-time water supply capacity deviation rate, which refers to the ratio of the absolute value of the difference between the water demand forecast data and the real-time water supply capacity data to the real-time water supply capacity data. Finally, the obtained real-time water supply capacity deviation rate is compared with the preset water supply capacity deviation rate threshold. In this embodiment, the water supply capacity deviation rate threshold is set to (0, 0.25], (0.25, 1], corresponding to the normal water supply state and the abnormal water supply state, respectively. For example, if the obtained real-time water supply capacity deviation rate is 0.2, the water supply capacity state is determined to be the normal water supply state. If the obtained real-time water supply capacity deviation rate is 0.3, it means that the real-time water supply capacity data is too large or too small, and the water supply capacity state is determined to be the abnormal water supply state. The water pump speed setting parameter is adjusted according to the real-time water supply capacity deviation rate, including increasing the speed or decreasing the speed.

[0049] The smart campus energy management optimization operation method disclosed in the present invention obtains real-time power demand forecast data and optimizes the processing by training the energy consumption demand model, compares it with the self-powered rated data, and optimizes the self-powered adjustment for the short-term loss state, thereby realizing the intelligent management and optimization of campus energy.

[0050] In the several embodiments provided in the present application, it should be understood that the disclosed devices and methods can be implemented in other ways. The device embodiments described above are only schematic. For example, the division of the units is only a logical function division. There may be other division methods in actual implementation, such as: multiple units or components can be combined, or can be integrated into another system, or some features can be ignored or not executed. In addition, the coupling, direct coupling, or communication connection between the components shown or discussed can be through some interfaces, and the indirect coupling or communication connection of the devices or units can be electrical, mechanical or other forms.

[0051] The units described above as separate components may or may not be physically separated, and the components displayed as units may or may not be physical units; they may be located in one place or distributed on multiple network units; some or all of the units may be selected according to actual needs to achieve the purpose of the present embodiment.

[0052] In addition, all functional units in the embodiments of the present invention may be integrated into one processing unit, or each unit may be separately used as a unit, or two or more units may be integrated into one unit; the above-mentioned integrated units may be implemented in the form of hardware or in the form of hardware plus software functional units.

[0053] Those skilled in the art can understand that: all or part of the steps of implementing the above method embodiments can be completed by hardware related to program instructions, the aforementioned program can be stored in a readable storage medium, and when the program is executed, it executes the steps of the above method embodiments; and the aforementioned storage medium includes: mobile storage devices, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), disks or optical disks, and other media that can store program codes.

[0054] Alternatively, if the above-mentioned integrated unit of the present invention is implemented in the form of a software function module and sold or used as an independent product, it can also be stored in a readable storage medium. Based on this understanding, the technical solution of the embodiment of the present invention can be essentially or partly reflected in the form of a software product that contributes to the prior art. The software product is stored in a storage medium and includes several instructions for a computer device (which can be a personal computer, server, or network device, etc.) to execute all or part of the methods described in each embodiment of the present invention. The aforementioned storage medium includes: various media that can store program codes, such as mobile storage devices, ROM, RAM, magnetic disks or optical disks.

Claims

1. A smart campus energy management optimization operation method, characterized in that: The following steps are involved: Obtain historical energy operation data and corresponding historical power consumption data within a preset campus range within multiple historical time periods, and process the historical energy operation data and the corresponding historical power consumption data to obtain an energy consumption demand model; Obtaining energy operation demand data within a preset time period within the preset campus range, processing the energy operation demand data, and obtaining corresponding real-time power demand forecast data; Obtain real-time weather information within the preset campus range and extract real-time environmental parameter data, compare with the benchmark data to obtain the real-time environmental deviation rate, and then process it in combination with the temperature control lighting area change rate and the real-time energy supply power fluctuation rate to obtain the power demand influencing factor; The real-time power demand forecast data is processed in combination with the power demand influencing factors to obtain real-time power demand forecast optimization data, and the total power demand forecast data of the smart campus is obtained by aggregation processing; The self-power supply rated data of the smart campus is compared with the total power demand forecast data to determine the self-power supply operation status, and optimize the self-power supply for the short-term and deficit status.

2. The smart campus energy management optimization operation method according to claim 1 is characterized in that: The obtaining of historical energy operation data and corresponding historical power consumption data within a preset campus range within multiple historical time periods, and processing the historical energy operation data and the corresponding historical power consumption data to obtain an energy consumption demand model includes: Obtain multiple historical energy operation data and corresponding multiple historical power consumption data within multiple historical time periods within a preset campus range; The historical energy operation data includes historical temperature adjustment data, historical temperature adjustment duration data, historical temperature adjustment area data, historical light brightness data, historical illumination area data and historical illumination duration data; The initial energy consumption demand model is trained based on multiple historical temperature control data, historical temperature control duration data, historical temperature control area data, historical light brightness data, historical illumination area data and historical illumination duration data combined with multiple corresponding historical power consumption data to obtain a trained energy consumption demand model.

3. The smart campus energy management optimization operation method according to claim 2 is characterized in that: The obtaining of energy operation demand data within a preset time period within the preset campus range, and processing according to the energy operation demand data to obtain corresponding real-time power demand forecast data, includes: Obtain energy operation demand data within a preset campus range within a preset time period, including temperature control demand data, temperature control duration preset data, temperature control area data, light brightness demand data, illumination area data, and illumination duration preset data; The temperature control demand data, temperature control time preset data, temperature control area data, light brightness demand data, illumination area data and illumination time preset data are input into the trained energy consumption demand model for processing to obtain real-time power demand forecast data.

4. The smart campus energy management optimization operation method according to claim 3 is characterized in that: The real-time weather information of the preset campus range is obtained and the real-time environmental parameter data is extracted, and the real-time environmental deviation rate is obtained by comparison with the benchmark data, and then the temperature control lighting area change rate and the real-time energy supply power fluctuation rate are processed to obtain the power demand influencing factor, including: Obtain real-time weather information within the preset campus range, and extract real-time environmental parameter data based on the real-time weather information, including real-time temperature data, real-time humidity data, and real-time brightness data; The real-time temperature data, the real-time humidity data and the real-time brightness data are respectively compared with the preset environmental reference temperature data, the environmental reference humidity data and the environmental reference brightness data to obtain the real-time environmental deviation rate, including the environmental temperature deviation rate, the environmental humidity deviation rate and the environmental brightness deviation rate; Obtain the temperature control lighting area change rate within the preset campus range, and obtain the real-time power supply fluctuation rate of the campus energy system; The ambient temperature deviation rate, ambient humidity deviation rate and ambient brightness deviation rate as well as the temperature-controlled lighting area change rate and real-time energy supply power fluctuation rate are input into a preset power demand impact assessment model for processing to obtain corresponding power demand impact factors.

5. The smart campus energy management optimization operation method according to claim 4 is characterized in that: The real-time power demand forecast data is processed in combination with the power demand influencing factors to obtain real-time power demand forecast optimization data, and the total power demand forecast data of the smart campus is obtained by aggregation processing, including: Processing the real-time power demand forecast data in combination with the power demand influencing factors to obtain real-time power demand forecast optimization data corresponding to a preset campus range; The real-time power demand forecast optimization data corresponding to multiple preset campus ranges are aggregated to obtain the total power demand forecast data corresponding to the smart campus.

6. The smart campus energy management optimization operation method according to claim 5 is characterized in that: The self-power supply rated data of the smart campus is compared with the total power demand forecast data to determine the self-power supply operation state, and the self-power supply optimization adjustment is performed for the short-term loss state, including: Obtain the self-power rating data of the smart campus; Comparing the self-powered rated data with the total power demand forecast data to obtain a power demand deviation rate; Performing a threshold comparison between the electric energy demand deviation rate and a preset electric energy demand deviation rate threshold; If it is less than or equal to the preset power demand deviation rate threshold, the self-power supply operation state is the self-power supply normal state; If it is greater than the preset power demand deviation rate threshold, the self-energy supply operation state is a self-energy shortfall state, and the self-energy supply operation plan is optimized and adjusted.

7. The smart campus energy management optimization operation method according to claim 6 is characterized in that: The step of comparing the self-powered rated data with the total power demand forecast data to obtain the power demand deviation rate includes: Comparing the self-powered rated data with the total power demand forecast data; If the self-powered rated data is greater than or equal to the total power demand forecast data, the self-powered operation state is the self-powered normal state; If the self-powered rated data is less than the total power demand forecast data, the power supply capacity rated data and the total power demand forecast data are processed to obtain the power demand deviation rate.

8. The smart campus energy management optimization operation method according to claim 7 is characterized in that: Also includes: Obtain water supply operation data within a preset campus range, process the water supply operation data, and obtain a water supply imbalance evaluation index; Comparing the water supply imbalance evaluation index with a preset water supply balance benchmark index to obtain a relative value of water supply balance; The relative value of the water supply balance is compared with the preset water supply balance threshold: If it is less than or equal to the preset water supply balance threshold, the water supply operation is judged to be normal; If it is greater than the preset water supply balance threshold, the water supply operation is judged to be abnormal, and the early warning response and water supply imbalance location information are output to the management end for display.

9. The smart campus energy management optimization operation method according to claim 8 is characterized in that: The step of obtaining water supply operation data within a preset campus range and processing the water supply operation data to obtain a water supply imbalance evaluation index includes: Obtain water supply operation data within a preset campus range, including water pressure difference and water flow difference; The water pressure difference and the water flow difference are input into a preset water supply balance evaluation model for processing to obtain a water supply imbalance evaluation index.

10. The smart campus energy management optimization operation method according to claim 9 is characterized in that: Also includes: Obtain the distance data between people and each light source within the preset campus range; Comparing the plurality of distance data with preset reference distance data respectively; If it is greater than the preset reference distance data, the light source shutdown response is activated, and the average brightness of the preset range of personnel is obtained; Comparing the brightness mean with the preset required brightness; If it is less than the preset standard brightness, the light source with the smallest distance data is activated to respond.

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