CN116993232B - Energy consumption management optimization method and system for comprehensive energy station - Google Patents
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Abstract
The invention provides an energy consumption management optimization method and system of a comprehensive energy station, which relate to the technical field of data processing and comprise the following steps: acquiring a plurality of air conditioning control subsystems in a central air conditioning energy station, wherein each subsystem corresponds to a control loop, is in bidirectional connection with a data acquisition card, performs state data acquisition, outputs an energy consumption state data set, inputs into a data center for storage, performs energy consumption data comparison, obtains data matching indexes corresponding to every two air conditioning control subsystems, performs classification output on the data matching indexes, performs matching with units of an energy consumption processing module, establishes a plurality of data transmission channels, performs optimization processing, generates energy consumption control parameters, and performs control on the central air conditioning energy station. The invention solves the technical problems of inaccurate energy consumption control and poor effect caused by the lack of an index system of an energy station in the prior art, and the extremely high accuracy requirement of the sensor for energy efficiency metering but the insufficient accuracy of the prior equipment.
Description
Technical Field
The invention relates to the technical field of data processing, in particular to an energy consumption management optimization method and system of a comprehensive energy station.
Background
The existing comprehensive energy station energy consumption management method mainly collects the energy consumption condition in the energy station without the functions of data analysis and model diagnosis, an index system of the energy station cannot be given, data are inaccurate, an comprehensive energy station energy efficiency collection and measurement system mainly measures energy efficiency of equipment in the energy station, the accuracy requirement on a sensor is very high, the accuracy of the equipment in the market cannot meet the requirement, and the system measurement error is large, so that the energy consumption management and control is inaccurate and the effect is poor.
Therefore, a certain liftable space exists for energy consumption management of the comprehensive energy station.
Disclosure of Invention
The application aims to solve the technical problems of inaccurate energy consumption management and control and poor effect caused by the fact that an index system of an energy station is lacked in the prior art, the accuracy requirement of energy efficiency measurement on a sensor is very high, and the accuracy of existing equipment cannot meet the requirement.
In view of the above problems, the present application provides an energy consumption management optimization method and system for an integrated energy station.
In a first aspect of the disclosure, an energy consumption management optimization method for an integrated energy station is provided, where the method includes: acquiring a plurality of air-conditioning control subsystems in a central air-conditioning energy station, wherein each air-conditioning control subsystem corresponds to a control loop, and the air-conditioning control subsystems are in bidirectional connection with a data acquisition card; according to the data acquisition card, acquiring state data of a control loop of each air conditioner control subsystem, outputting an energy consumption state data set, and inputting the energy consumption state data set into a data center for storage; comparing the energy consumption data of the energy consumption state data sets to obtain data matching indexes corresponding to every two air conditioner control subsystems, classifying the plurality of air conditioner control subsystems according to the data matching indexes, and outputting multiple types of subsystems; matching the types in the multi-type subsystem with units of an energy consumption processing module of the data center, and establishing a plurality of data transmission channels, wherein the data transmission channels are data transmission channels after the data center is connected with the units of the energy consumption processing module; and optimizing the energy consumption state data sets according to the plurality of data transmission channels, generating energy consumption control parameters according to the processed energy consumption state data sets, and controlling the central air-conditioning energy station according to the energy consumption control parameters.
In another aspect of the disclosure, there is provided an energy consumption management optimization system of an integrated energy station, the system being used in the above method, the system comprising: the system comprises a subsystem acquisition unit, a data acquisition card and a control unit, wherein the subsystem acquisition unit is used for acquiring a plurality of air-conditioning control subsystems in a central air-conditioning energy station, each air-conditioning control subsystem corresponds to one control loop, and the plurality of air-conditioning control subsystems are in bidirectional connection with the data acquisition card; the state data acquisition unit is used for acquiring state data of a control loop of each air conditioner control subsystem according to the data acquisition card, outputting an energy consumption state data set, and inputting the energy consumption state data set into a data center for storage; the energy consumption data comparison unit is used for comparing the energy consumption data of the energy consumption state data set to obtain data matching indexes corresponding to every two air conditioner control subsystems, classifying the plurality of air conditioner control subsystems according to the data matching indexes and outputting multiple types of subsystems; the transmission channel establishment unit is used for matching with the units of the energy consumption processing module of the data center according to the categories in the multi-category subsystem to establish a plurality of data transmission channels, wherein the data transmission channels are data transmission channels after the data center is connected with the units of the energy consumption processing module; and the energy station management and control unit is used for optimizing the energy consumption state data sets according to the data transmission channels, generating energy consumption management and control parameters according to the processed energy consumption state data sets, and managing and controlling the central air conditioner energy station according to the energy consumption management and control parameters.
One or more technical solutions provided in the present application have at least the following technical effects or advantages: for a plurality of air conditioner control subsystems, bidirectional connection is realized through a data acquisition card, and state data acquisition is carried out on a control loop of each air conditioner control subsystem by utilizing the data acquisition card, so that unified data acquisition of the plurality of air conditioner control subsystems can be realized, and energy consumption management among the subsystems is coordinated; the energy consumption state data set is optimized, the air conditioner control subsystems are classified based on the data matching indexes, a plurality of data transmission channels are established by matching with units of the energy consumption processing module, accurate energy consumption data can be provided by establishing accurate data transmission channels, and the problem of inaccurate management and control caused by inaccurate or missing data is solved; and generating energy consumption control parameters by using the processed energy consumption state data set, and controlling the central air conditioner energy station by using the parameters, so that the energy consumption can be optimally managed according to accurate energy consumption data, and the effective control and optimization effects of the energy consumption are realized. In summary, the energy consumption management optimization method of the comprehensive energy station solves the coordination problem among a plurality of air conditioner control subsystems by improving the data acquisition, transmission and processing flow, and realizes the accurate management and optimization effects on the energy consumption of the central air conditioner energy station by accurate data and energy consumption management and control parameters.
The foregoing description is only an overview of the technical solutions of the present application, and may be implemented according to the content of the specification in order to make the technical means of the present application more clearly understood, and in order to make the above-mentioned and other objects, features and advantages of the present application more clearly understood, the following detailed description of the present application will be given.
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Fig. 1 is a schematic flow chart of an energy consumption management optimization method of an integrated energy station according to an embodiment of the present application.
Fig. 2 is a schematic structural diagram of an energy consumption management optimization system of an integrated energy station according to an embodiment of the present application.
Reference numerals illustrate: the system comprises a subsystem acquisition unit 10, a state data acquisition unit 20, an energy consumption data comparison unit 30, a transmission channel establishment unit 40 and an energy station management and control unit 50.
Detailed Description
According to the energy consumption management optimization method for the comprehensive energy station, the technical problems that an index system of the comprehensive energy station is lacked in the prior art, the accuracy requirement of energy efficiency measurement on a sensor is very high, the accuracy of existing equipment cannot meet the requirement, and the energy consumption management and control are inaccurate and poor in effect are solved.
Having described the basic principles of the present application, various non-limiting embodiments of the present application will now be described in detail with reference to the accompanying drawings.
An embodiment, as shown in fig. 1, provides an energy consumption management optimization method of an integrated energy station, where the method includes: acquiring a plurality of air-conditioning control subsystems in a central air-conditioning energy station, wherein each air-conditioning control subsystem corresponds to a control loop, and the air-conditioning control subsystems are in bidirectional connection with a data acquisition card; the system comprises a central air conditioning energy station, a data acquisition card, a control system and a control system, wherein a plurality of air conditioning control subsystems in the central air conditioning energy station are integrated, each subsystem is responsible for controlling an air conditioning system of a building, each air conditioning subsystem is in bidirectional connection with the data acquisition card, the data acquisition card is hardware equipment for acquiring state data of the air conditioning subsystems, and has the main functions of acquiring relevant parameters such as temperature, pressure and the like in real time and acquiring real-time running state parameters of the equipment, and each state change of the data is important, so that the monitoring and data acquisition of the air conditioning control subsystems can be realized through the bidirectional connection with the data acquisition card.
According to the data acquisition card, acquiring state data of a control loop of each air conditioner control subsystem, outputting an energy consumption state data set, and inputting the energy consumption state data set into a data center for storage; the data acquisition card is connected with each air conditioner control subsystem to acquire state data in a control loop of the data acquisition card, wherein the state data comprises information related to energy consumption such as temperature, pressure, cold and hot quantity, electric quantity and the like, the acquired state data is integrated into an energy consumption state data set, and the data set comprises the energy consumption state information of each air conditioner control subsystem and is used for energy consumption management, analysis and optimization.
The generated energy consumption state data set is transmitted to a data center for storage, the data center is a server for centralized management and data storage, high-efficiency data processing and access capacity are provided, and the energy consumption state data set is stored and managed through the data center, including data backup, index establishment, authority control and the like, so that the safety and reliability of data can be ensured, and the follow-up inquiry and analysis of the data are facilitated.
Comparing the energy consumption data of the energy consumption state data sets to obtain data matching indexes corresponding to every two air conditioner control subsystems, classifying the plurality of air conditioner control subsystems according to the data matching indexes, and outputting multiple types of subsystems; and comparing the energy consumption data of each two air conditioner control subsystems in the energy consumption state data set, for example, comparing by adopting a similarity calculation method to quantify the difference between different air conditioner control subsystems, and obtaining data matching indexes between each two air conditioner control subsystems through calculation after the energy consumption data comparison, wherein the indexes can be scores, such as the similarity of energy consumption curves, for measuring the energy consumption difference between the two subsystems.
Based on the calculated data matching indexes, classifying the plurality of air-conditioning control subsystems, according to a preset similarity interval, for example, dividing the air-conditioning control subsystems into an interval every 0.1 increment, taking the subsystems with similar similarity in the same interval as the subsystems with similar energy consumption, dividing the subsystems into the same category, outputting the classified plurality of subsystems to form a plurality of types of subsystems, wherein each category comprises an air-conditioning control subsystem set with similar energy consumption.
Matching the types in the multi-type subsystem with units of an energy consumption processing module of the data center, and establishing a plurality of data transmission channels, wherein the data transmission channels are data transmission channels after the data center is connected with the units of the energy consumption processing module; according to the classification result of the multi-class subsystem, the units of the energy consumption processing modules of the data center are matched with each class, the energy consumption processing module unit corresponding to each class is responsible for processing the energy consumption data of the class, and according to the matching result, a plurality of data transmission channels are established, and the data transmission channels are used for connecting the data center with the corresponding energy consumption processing module units and are used for data transmission and exchange.
After the data transmission channels are established, the corresponding types of energy consumption data in the energy consumption state data set can be transmitted through the channels, and the data center extracts the energy consumption data from the energy consumption state data set and sends the energy consumption data to the corresponding energy consumption processing module units through the corresponding data transmission channels.
Optimizing the energy consumption state data sets according to the plurality of data transmission channels, generating energy consumption control parameters according to the processed energy consumption state data sets, and controlling the central air-conditioning energy station according to the energy consumption control parameters; through the established multiple data transmission channels, the data center receives the energy consumption state data sets from each energy consumption processing module unit, each data transmission channel corresponds to one type of energy consumption data, multiple models can be formed according to the characteristics of energy consumption performance aiming at different types of energy consumption data, the same data processing mode is adopted for the same type of energy consumption data, the data processing efficiency is improved, and the global optimum is ensured by local optimum. After the optimization processing, the data center generates energy consumption management and control parameters according to the processed energy consumption state data set, and the energy consumption management and control parameters are used for guiding management and control operation on the central air conditioner energy station.
Further, the method further comprises: establishing an energy consumption processing module, wherein the energy consumption processing module comprises a plurality of processing channels, and each processing channel is used for correspondingly processing the energy consumption index of the corresponding grade; performing energy consumption index calculation on the energy consumption state data set to obtain energy consumption levels; and performing level matching on the air conditioner control subsystems and the processing channels according to the energy consumption levels, and establishing channels with the processing units with the same energy consumption level by taking the air conditioner control subsystems with the same energy consumption level as a class.
The method comprises the steps of designing an energy consumption processing module, determining the number of required processing channels and the functions and processing methods of each processing channel according to the requirement of energy consumption management, dividing the energy consumption processing module into different grades according to the characteristics and differences of energy consumption data, such as the magnitude, the change amplitude and other factors of energy consumption, dividing the processing channels according to the grades of energy consumption indexes, wherein each grade corresponds to one processing channel and is responsible for processing the energy consumption indexes in the range of the grade, and selecting a proper processing method for each processing channel to realize corresponding functional modules, such as data preprocessing, feature extraction, model establishment, energy consumption analysis and the like, and processing the energy consumption indexes of the corresponding grade.
Determining energy consumption indexes, including the shape, peak energy consumption, average energy consumption, volatility and the like of an energy consumption curve, carrying out statistics and calculation on an energy consumption state data set based on the energy consumption indexes, for example, calculating the energy consumption indexes by calculating statistics such as the maximum value, the average value and the variance of the energy consumption curve, dividing each sample in the energy consumption state data set into different energy consumption levels according to the calculated energy consumption index values, and determining the range of each energy consumption level.
And obtaining the energy consumption level of each air conditioner control subsystem through energy consumption index calculation, dividing the plurality of air conditioner control subsystems into different categories according to the energy consumption level, and classifying the air conditioner control subsystems with the same energy consumption level into the same category so as to facilitate the matching of processing channels. And matching the corresponding air conditioner control subsystem with the corresponding processing channel according to each energy consumption level, namely, establishing connection between the air conditioner control subsystem with the same energy consumption level and the processing channel with the same energy consumption level. And establishing a plurality of channel connections according to the matching result, wherein each connection channel connects a plurality of air conditioner control subsystems belonging to the same energy consumption level with the processing unit and is used for transmitting and exchanging energy consumption data.
Further, the energy consumption state data set is input into a data center for storage, and the method further comprises the steps of: inputting the energy consumption state data set into a data center for storage, and detecting data synchronism, data instantaneity and data continuity of each energy consumption source in the energy consumption state data set to obtain a synchronism detection result, a instantaneity detection result and a continuity detection result; positioning abnormal detection data according to the synchronous detection result, the real-time detection result and the continuity detection result; and optimally correcting the abnormal detection data by the plurality of processing channels, and outputting the corrected energy consumption state data set.
Performing data synchronism detection on each energy consumption source in the energy consumption state data set, which means that whether the data collected by the source are synchronous with the data of other collected sources or not is verified, wherein the synchronism detection method comprises data timestamp comparison, data interval comparison and the like; carrying out data real-time detection on each energy consumption source, and determining whether the update speed of the energy consumption data meets the real-time requirement, for example, the data needs to be acquired once in the minimum of 1 second, and the real-time detection method comprises timestamp analysis, data delay analysis and the like; data continuity testing is performed on each energy consumption source to verify whether the data is continuous and uninterrupted, for example, the required time interval cannot exceed 0.1 seconds, and the continuity testing method comprises data missing detection, data interruption detection, data abnormality detection and the like.
According to the detection of each energy consumption source in the energy consumption state data set, a synchronism detection result, a real-time detection result and a continuity detection result are obtained, and the results show the synchronism, the real-time performance and the continuity of the data of each energy consumption source.
Analyzing the synchronism detection result, and identifying energy consumption data which fails to pass the synchronism detection, wherein the data possibly have the condition of being inconsistent with other data, and represents the asynchronism between data sources; analyzing the real-time detection result to find out the energy consumption data which cannot meet the real-time requirement, wherein the data cannot be updated in time due to delay or other reasons, and cannot reflect the current real energy consumption level; and analyzing the continuity detection result to determine whether intermittent or missing energy consumption data exist, wherein the data can be interrupted, missing or abnormal in the acquisition process.
And marking the data which fails to pass the detection of the synchronism, the real-time property or the continuity as abnormal detection data by combining the analysis results, wherein the abnormal data reflect the inconsistency, the delay or the intermittent condition of the energy consumption data.
The plurality of processing channels are classified according to the categories of the energy consumption data, namely the processing channels with the same processing mode are divided into the same category, so that the correction of the abnormal detection data with the same category by adopting the same processing mode can be ensured. And for each abnormal detection data point, performing optimization correction in the processing channels of the category, and performing data cleaning, interpolation, repair or other corresponding correction processing operations on the corresponding energy consumption data category according to the specific processing mode of each processing channel. Updating the optimized and corrected energy consumption data into a state data set, and outputting corrected energy consumption state data sets which contain the energy consumption state information optimized and corrected by the processing channel so as to perfect and repair abnormal data in the original data set.
Further, the method further comprises optimizing the anomaly detection data with the plurality of processing channels and outputting the processed energy consumption state data set, and the method further comprises: identifying according to the abnormality detection data, stripping the abnormality detection data from the energy consumption state data set, and outputting a stripped energy consumption state sample; according to the energy consumption levels in the plurality of processing channels, an energy consumption data source is established, and the data acquisition card is controlled by the energy consumption data source to acquire data; and calling a historical energy consumption state sample from the data acquisition card according to the energy consumption data source, carrying out prediction correction on the stripped energy consumption state data set according to the change characteristics of the historical energy consumption state sample, and outputting the corrected energy consumption state data set.
The anomaly detection data comprises data points with unmatched synchronism, delayed real-time performance or interrupted continuity, the anomaly detection data are stripped from the energy consumption state data set according to the identification of the anomaly detection data, namely the anomaly detection data are separated from the original data set, and the stripped data are used as independent energy consumption state samples for subsequent processing and analysis.
For each energy consumption level a corresponding energy consumption data source is established, which for each energy consumption level represents a specific class of energy consumption scale or configuration, e.g. in load 30 mode and in load 70 mode, the energy consumption data source may take into account the differences in the number of differently activated pumps or other energy consumption components. And controlling the related data acquisition card to acquire data by setting parameters of the data acquisition card, configuring sampling frequency, time window and the like by using the established energy consumption data source.
According to the established energy consumption data source, historical energy consumption state samples are called from the data acquisition card, and the samples represent the time-dependent change condition of the past energy consumption state. And carrying out change characteristic analysis on the called historical energy consumption state sample, identifying the characteristics of change rules, trends, periodicity and the like of the energy consumption state in the historical sample through statistics and data mining, and restoring, interpolating or filling abnormal energy consumption state data by utilizing the mode, trend or periodicity information in the historical sample, so that the abnormal data can be restored, and the abnormal data is more in accordance with the actual energy consumption state. And updating the corrected energy consumption state data into a data set, and outputting the corrected energy consumption state data set, wherein the energy consumption state data set comprises the energy consumption data subjected to prediction correction processing and can be used for subsequent energy consumption analysis, management and control and optimization.
Abnormal data is recovered or missing data is improved by analyzing the change rule of the historical sample, the accuracy and the integrity of the energy consumption data are improved, and finally a corrected energy consumption state data set is output, so that a more reliable data basis is provided for energy consumption management and optimization decision.
Further, the method further comprises: identifying the historical energy consumption state sample, and determining a first energy consumption source with the largest energy consumption ratio and a correlation coefficient set for representing the variation correlation among the energy consumption sources; predicting a first energy consumption source in the stripped energy consumption state data set to obtain first prediction data; predicting residual energy consumption sources according to the correlation coefficient set and the first prediction data to obtain residual prediction data; and outputting the corrected energy consumption state data set according to the first prediction data and the residual prediction data.
And aiming at the historical energy consumption state sample, calculating the duty ratio of each energy consumption source in the total energy consumption by comparing the energy consumption value of each energy consumption source with the total energy consumption value, and determining the first energy consumption source with the maximum energy consumption duty ratio according to the duty ratio.
For each energy consumption source, a correlation coefficient, such as a pearson correlation coefficient, is calculated with other energy consumption sources to characterize the varying correlation between them, the correlation coefficient reflecting the degree of linear correlation between the different energy consumption sources.
Historical data of a first energy consumption source is obtained from the stripped energy consumption state data set, the historical data are used for constructing a prediction model and performing model training, a proper prediction model such as ARIMA (time series analysis model) is selected based on the characteristics and data distribution of the first energy consumption source, and the historical data are used for performing model training to learn information such as data modes, trends, periodicity and the like.
Applying the trained prediction model to a future time period of the first energy consumption source, filling corresponding variables and parameters according to the characteristics of the selected model, and predicting the numerical value or trend of the first energy consumption source in the future time period, wherein a prediction result can be in the forms of single-point prediction, interval prediction or probability distribution and the like. Based on the application of the predictive model, a predicted value of the first energy consumption source in a future time period, i.e. first predicted data, is obtained, which predicted value represents the expected change situation of the first energy consumption source, which is derived from the historical data and the selected model.
And predicting the residual energy consumption sources according to the calculated correlation coefficient sets among the energy consumption sources, and particularly selecting corresponding correlation coefficients from the correlation coefficient sets according to the correlation coefficients between the residual energy consumption sources and the first energy consumption sources so as to reflect the degree of correlation among the residual energy consumption sources and the first energy consumption sources. The selected correlation coefficient and the first prediction data are input into a prediction formula, prediction calculation is carried out on the residual energy consumption source, and the change trend of the residual energy consumption source is deduced based on the change of the first prediction data and the correlation degree between the first prediction data and the residual energy consumption source. This step is repeated for the remaining other sources of energy consumption to obtain remaining prediction data.
Inputting the selected correlation coefficient and the first prediction data into a prediction formula, and performing prediction calculation on the residual energy consumption sources, wherein the expression is as follows:the method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>Is->In the individual processing channels->The source of residual energy consumption>Is>Is->The first part of the processing channel>The source of residual energy consumption>Corresponding stripped energy consumption state data set,/->Is based on->The first part of the processing channel>The source of residual energy consumption>Training the acquired matching state data set with the corresponding historical energy consumption state sample toFor prediction conditions, in->In order to optimize the prediction of the variables,，/>is->Total number of energy consumption sources in each processing channel, < >>As a source of the first energy consumption,is the first energy source->And->The source of residual energy consumption>Correlation coefficient between the two.
And obtaining corresponding residual prediction data according to the correlation coefficient and the first prediction data by processing each residual energy source one by one, wherein the data represent expected change conditions of the residual energy sources after the joint prediction according to the correlation information.
And integrating the first prediction data and the residual prediction data with the original energy consumption state data set to ensure that the prediction data corresponds to a corresponding time interval, so that consistency with other data in aspects of time, energy consumption sources and the like can be maintained. And correcting the abnormal value, the missing value or the inaccurate value in the energy consumption state data set according to the integrated prediction data, including repairing the abnormal value, complementing the missing value and the like, so as to improve the accuracy and the integrity of the energy consumption state data. And taking the corrected energy consumption state data set as an output result, wherein the corrected data set reflects the energy consumption state information after the original data is corrected through the predicted data, and more accurately reflects the real energy consumption condition.
Further, the method further comprises: acquiring an air conditioning mode of the central air conditioning energy station, wherein the air conditioning mode comprises an air conditioning refrigeration mode and an air conditioning heating mode; and detecting the real-time air conditioning mode of the central air conditioning energy station, and switching the sensor connected with the data acquisition card according to the real-time air conditioning mode.
And acquiring an air conditioning mode of the central air conditioning energy station according to system configuration and operation requirements, wherein the air conditioning mode comprises a refrigerating mode and a heating mode.
The current operation state and the air conditioning mode of the air conditioning energy station are monitored in real time by a control system connected with the central air conditioning energy station in a mode of inquiring the equipment state, reading sensor data or receiving feedback from the control system. Based on the detected real-time air conditioning mode, corresponding switching rules and configuration schemes are defined, which will guide the dynamic switching of the sensors connected to the data acquisition card. And switching the sensor connected with the data acquisition card by using a proper control means such as circuit switching and software command according to the air conditioning mode and the switching rule so as to ensure that the acquired data is matched with the current air conditioning mode to acquire accurate data related to the specific operation mode.
In summary, the energy consumption management optimization method and system for the comprehensive energy station provided by the embodiment of the application have the following technical effects: 1. for a plurality of air conditioner control subsystems, bidirectional connection is realized through a data acquisition card, and state data acquisition is carried out on a control loop of each air conditioner control subsystem by utilizing the data acquisition card, so that unified data acquisition of the plurality of air conditioner control subsystems can be realized, and energy consumption management among the subsystems is coordinated; 2. the energy consumption state data set is optimized, the air conditioner control subsystems are classified based on the data matching indexes, a plurality of data transmission channels are established by matching with units of the energy consumption processing module, accurate energy consumption data can be provided by establishing accurate data transmission channels, and the problem of inaccurate management and control caused by inaccurate or missing data is solved; 3. and generating energy consumption control parameters by using the processed energy consumption state data set, and controlling the central air conditioner energy station by using the parameters, so that the energy consumption can be optimally managed according to accurate energy consumption data, and the effective control and optimization effects of the energy consumption are realized.
In summary, the energy consumption management optimization method of the comprehensive energy station solves the coordination problem among a plurality of air conditioner control subsystems by improving the data acquisition, transmission and processing flow, and realizes the accurate management and optimization effects on the energy consumption of the central air conditioner energy station by accurate data and energy consumption management and control parameters.
In a second embodiment, based on the same inventive concept as the energy consumption management optimization method of an integrated energy station in the foregoing embodiment, as shown in fig. 2, the present application provides an energy consumption management optimization system of an integrated energy station, where the system includes: the subsystem acquisition unit 10 is configured to acquire a plurality of air-conditioning subsystems in a central air-conditioning energy station, where each air-conditioning control subsystem corresponds to a control loop, and the plurality of air-conditioning subsystems are connected with the data acquisition card in two directions; the state data acquisition unit 20 is used for acquiring state data of a control loop of each air conditioner control subsystem according to a data acquisition card, outputting an energy consumption state data set, and inputting the energy consumption state data set into a data center for storage; the energy consumption data comparison unit 30 is used for comparing the energy consumption data of the energy consumption state data set to obtain data matching indexes corresponding to every two air conditioner control subsystems, classifying the air conditioner control subsystems according to the data matching indexes and outputting multiple types of subsystems; a transmission channel establishing unit 40, where the transmission channel establishing unit 40 is configured to match with a unit of an energy consumption processing module of the data center according to a category in the multi-category subsystem, and establish a plurality of data transmission channels, where the data transmission channels are data transmission channels after connection between the data center and the unit of the energy consumption processing module; and the energy station control unit 50 is used for optimizing the energy consumption state data sets according to the plurality of data transmission channels, generating energy consumption control parameters according to the processed energy consumption state data sets, and controlling the central air conditioner energy station according to the energy consumption control parameters.
Further, the system also comprises a channel establishment module for executing the following operation steps: establishing an energy consumption processing module, wherein the energy consumption processing module comprises a plurality of processing channels, and each processing channel is used for correspondingly processing the energy consumption index of the corresponding grade; performing energy consumption index calculation on the energy consumption state data set to obtain energy consumption levels; and performing level matching on the air conditioner control subsystems and the processing channels according to the energy consumption levels, and establishing channels with the processing units with the same energy consumption level by taking the air conditioner control subsystems with the same energy consumption level as a class.
Further, the system also comprises a data optimization correction module for executing the following operation steps: inputting the energy consumption state data set into a data center for storage, and detecting data synchronism, data instantaneity and data continuity of each energy consumption source in the energy consumption state data set to obtain a synchronism detection result, a instantaneity detection result and a continuity detection result; positioning abnormal detection data according to the synchronous detection result, the real-time detection result and the continuity detection result; and optimally correcting the abnormal detection data by the plurality of processing channels, and outputting the corrected energy consumption state data set.
Further, the system also includes a predictive correction module to perform the following operational steps: identifying according to the abnormality detection data, stripping the abnormality detection data from the energy consumption state data set, and outputting a stripped energy consumption state sample; according to the energy consumption levels in the plurality of processing channels, an energy consumption data source is established, and the data acquisition card is controlled by the energy consumption data source to acquire data; and calling a historical energy consumption state sample from the data acquisition card according to the energy consumption data source, carrying out prediction correction on the stripped energy consumption state data set according to the change characteristics of the historical energy consumption state sample, and outputting the corrected energy consumption state data set.
Further, the system also comprises a data set output module for executing the following operation steps: identifying the historical energy consumption state sample, and determining a first energy consumption source with the largest energy consumption ratio and a correlation coefficient set for representing the variation correlation among the energy consumption sources; predicting a first energy consumption source in the stripped energy consumption state data set to obtain first prediction data; predicting residual energy consumption sources according to the correlation coefficient set and the first prediction data to obtain residual prediction data; and outputting the corrected energy consumption state data set according to the first prediction data and the residual prediction data.
Further, predicting the residual energy consumption source according to the correlation coefficient set and the first prediction data, wherein the expression is as follows:the method comprises the steps of carrying out a first treatment on the surface of the Wherein (1)>Is->In the individual processing channels->The source of residual energy consumption>Is>Is->The first part of the processing channel>The source of residual energy consumption>Corresponding stripped energy consumption state data set,/->Is based on->The first part of the processing channel>The source of residual energy consumption>Training the acquired matching state data set with the corresponding historical energy consumption state sample toFor prediction conditions, in->In order to optimize the prediction of the variables,，/>is->Total number of energy consumption sources in each processing channel, < >>As a source of the first energy consumption,is the first energy source->And->The source of residual energy consumption>Correlation coefficient between the two.
Further, the system also includes a sensor switching module to perform the following operation steps: acquiring an air conditioning mode of the central air conditioning energy station, wherein the air conditioning mode comprises an air conditioning refrigeration mode and an air conditioning heating mode; and detecting the real-time air conditioning mode of the central air conditioning energy station, and switching the sensor connected with the data acquisition card according to the real-time air conditioning mode.
The foregoing detailed description of the method for optimizing energy consumption management of an integrated energy station will be clear to those skilled in the art, and the description of the device disclosed in this embodiment is relatively simple, and the relevant places refer to the description of the method section.
The previous description of the disclosed embodiments is provided to enable any person skilled in the art to make or use the present application. Various modifications to these embodiments will be readily apparent to those skilled in the art, and the generic principles defined herein may be applied to other embodiments without departing from the spirit or scope of the application. Thus, the present application is not intended to be limited to the embodiments shown herein but is to be accorded the widest scope consistent with the principles and novel features disclosed herein.
Claims (2)
1. An energy consumption management optimization method for an integrated energy station, the method comprising:
acquiring a plurality of air-conditioning control subsystems in a central air-conditioning energy station, wherein each air-conditioning control subsystem corresponds to a control loop, and the air-conditioning control subsystems are in bidirectional connection with a data acquisition card;
according to the data acquisition card, acquiring state data of a control loop of each air conditioner control subsystem, outputting an energy consumption state data set, and inputting the energy consumption state data set into a data center for storage;
comparing the energy consumption data of the energy consumption state data sets to obtain data matching indexes corresponding to every two air conditioner control subsystems, classifying the plurality of air conditioner control subsystems according to the data matching indexes, and outputting multiple types of subsystems;
matching the types in the multi-type subsystem with units of an energy consumption processing module of the data center, and establishing a plurality of data transmission channels, wherein the data transmission channels are data transmission channels after the data center is connected with the units of the energy consumption processing module;
optimizing the energy consumption state data sets according to the plurality of data transmission channels, generating energy consumption control parameters according to the processed energy consumption state data sets, and controlling the central air-conditioning energy station according to the energy consumption control parameters;
the method further comprises the steps of:
establishing an energy consumption processing module, wherein the energy consumption processing module comprises a plurality of processing channels, and each processing channel is used for correspondingly processing the energy consumption index of the corresponding grade;
performing energy consumption index calculation on the energy consumption state data set to obtain energy consumption levels;
performing level matching on the air conditioning control subsystems and the processing channels according to the energy consumption levels, and establishing channels with the processing units with the same energy consumption level by taking the air conditioning control subsystems with the same energy consumption level as a class;
the energy consumption state data set is input into a data center for storage, and the method further comprises the steps of:
inputting the energy consumption state data set into a data center for storage, and detecting data synchronism, data instantaneity and data continuity of each energy consumption source in the energy consumption state data set to obtain a synchronism detection result, a instantaneity detection result and a continuity detection result;
positioning abnormal detection data according to the synchronous detection result, the real-time detection result and the continuity detection result;
optimizing and correcting the abnormality detection data by the plurality of processing channels, and outputting the corrected energy consumption state data set;
optimizing the abnormality detection data by the plurality of processing channels and outputting the processed energy consumption state data set, wherein the method further comprises:
identifying according to the abnormality detection data, stripping the abnormality detection data from the energy consumption state data set, and outputting a stripped energy consumption state sample;
according to the energy consumption levels in the plurality of processing channels, an energy consumption data source is established, and the data acquisition card is controlled by the energy consumption data source to acquire data;
according to the energy consumption data source, calling a historical energy consumption state sample from the data acquisition card, carrying out prediction correction on the stripped energy consumption state data set according to the change characteristics of the historical energy consumption state sample, and outputting the corrected energy consumption state data set;
identifying the historical energy consumption state sample, and determining a first energy consumption source with the largest energy consumption ratio and a correlation coefficient set for representing the variation correlation among the energy consumption sources;
predicting a first energy consumption source in the stripped energy consumption state data set to obtain first prediction data;
predicting residual energy consumption sources according to the correlation coefficient set and the first prediction data to obtain residual prediction data;
outputting the corrected energy consumption state data set according to the first prediction data and the residual prediction data;
predicting the residual energy consumption source according to the correlation coefficient set and the first prediction data, wherein the expression is as follows:
P k (x i )＝ρ(x,x i )|w k (x i )-w k '(x i )| min
wherein P (x) i ) Source x of the ith remaining energy consumption in the kth processing channel i Is the predictive function, w k (x i ) For the ith remaining energy consumption source x in the kth processing channel i Corresponding stripped energy consumption state data set, w k ′(x i ) Based on the ith remaining energy consumption source x in the kth processing channel i Training the acquired matching state data set by corresponding historical energy consumption state samples with |w k (x i )-w k ′(x i )| min For the prediction condition, ρ (x, x i ) For optimizing variables, i= {0,1, 2..n-1 }, n is the total number of energy consumption sources in the kth processing channel, x is the first energy consumption source, ρ (x, x i ) For the first energy consumption source x and the ith residual energy consumption source x i Correlation coefficients between;
acquiring an air conditioning mode of the central air conditioning energy station, wherein the air conditioning mode comprises an air conditioning refrigeration mode and an air conditioning heating mode;
and detecting the real-time air conditioning mode of the central air conditioning energy station, and switching the sensor connected with the data acquisition card according to the real-time air conditioning mode.
2. An energy consumption management optimization system for an integrated energy station, for implementing the energy consumption management optimization method for an integrated energy station of claim 1, comprising:
the system comprises a subsystem acquisition unit, a data acquisition card and a control unit, wherein the subsystem acquisition unit is used for acquiring a plurality of air-conditioning control subsystems in a central air-conditioning energy station, each air-conditioning control subsystem corresponds to one control loop, and the plurality of air-conditioning control subsystems are in bidirectional connection with the data acquisition card;
the state data acquisition unit is used for acquiring state data of a control loop of each air conditioner control subsystem according to the data acquisition card, outputting an energy consumption state data set, and inputting the energy consumption state data set into a data center for storage;
the energy consumption data comparison unit is used for comparing the energy consumption data of the energy consumption state data set to obtain data matching indexes corresponding to every two air conditioner control subsystems, classifying the plurality of air conditioner control subsystems according to the data matching indexes and outputting multiple types of subsystems;
the transmission channel establishment unit is used for matching with the units of the energy consumption processing module of the data center according to the categories in the multi-category subsystem to establish a plurality of data transmission channels, wherein the data transmission channels are data transmission channels after the data center is connected with the units of the energy consumption processing module;
and the energy station management and control unit is used for optimizing the energy consumption state data sets according to the data transmission channels, generating energy consumption management and control parameters according to the processed energy consumption state data sets, and managing and controlling the central air conditioner energy station according to the energy consumption management and control parameters.
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