Building equipment energy consumption statistics and optimization method

Abstract

The invention discloses a building equipment energy consumption statistics and optimization method, and relates to the technical field of building energy consumption optimization, and the method comprises the steps: collecting the energy consumption data of building equipment, and carrying out the preprocessing, and the energy consumption data comprises the equipment power consumption, the operation time, and environment parameters; inputting the preprocessed energy consumption data into the constructed energy consumption reference model to obtain an energy efficiency deviation index; calculating an equipment energy efficiency score based on the energy efficiency deviation index in combination with the environmental parameters; and performing hierarchical management on the building equipment according to the equipment energy efficiency score, and when the equipment energy efficiency score is lower than a preset threshold value, triggering equipment maintenance early warning and generating an equipment optimization scheme. According to the invention, by establishing a dynamic grading threshold and a differentiated management and control mechanism, intelligent grading management of equipment is realized, and management standards can be adaptively adjusted according to historical data distribution characteristics.

Description

Technical Field

[0001] The present invention relates to the technical field of building energy consumption optimization, in particular to a method for statistically analyzing and optimizing building equipment energy consumption. Background Art

[0002] Against the backdrop of the increasing importance of building energy management, refined energy consumption monitoring and optimized regulation of building equipment have become a hot topic in current research and practice. However, most existing energy management systems are still at the stage of basic statistical analysis and manual decision-making, with problems such as single data dimension, low coupling of environmental factors, and lack of dynamic optimization feedback. Especially in complex building environments where multiple devices operate in coordination, energy consumption assessment based solely on the total power consumption of equipment is difficult to reveal differences in operating efficiency and potential energy efficiency issues. At the same time, current systems generally lack intelligent energy efficiency grading and optimization mechanisms, and are unable to effectively guide equipment maintenance and energy-saving regulation, which in turn limits the improvement of the overall energy consumption level of buildings. Therefore, how to establish an accurate energy efficiency model based on multi-dimensional data, identify energy efficiency deviations in real time, and use this as a basis for equipment optimization is a key technical problem that needs to be solved urgently.

[0003] CN117455724A discloses a building energy consumption analysis method and system based on multi-level network nodes. By collecting and hierarchically transmitting the energy consumption data of public buildings, it can perform real-time analysis of the energy consumption data of each sampling point during actual operation, promptly discover and report energy consumption anomalies, and predict future energy consumption trends, thereby providing a basis for decision-making for managers. This solution improves the timeliness and comprehensiveness of building energy consumption monitoring, and the evaluation of building energy consumption status is more accurate, providing data support for energy-saving transformation. However, this technology mainly focuses on the aggregation and trend prediction of energy consumption data at the overall or regional level of the building, lacks in-depth exploration and classification management mechanism of the operating efficiency of specific equipment, and it is difficult to provide optimization strategies for inefficient equipment. At the same time, it has not established an energy efficiency model that integrates multi-dimensional parameters, and the impact of environmental conditions on equipment performance has not been fully reflected, so the support for equipment-level energy consumption regulation is limited.

[0004] CN116131253A proposes a method for optimizing building energy consumption control. Its innovation lies in combining building energy consumption control with power system scheduling. By collecting building load fluctuation data and generator set output fluctuation data, a scheduling optimization model is constructed to generate an energy consumption control strategy to achieve dynamic balance and optimal distribution of energy consumption among buildings. This method effectively takes into account the dynamic changes on both the supply and demand sides, and improves the systematicness and rationality of energy consumption distribution. However, the invention focuses on the control of electricity load at the overall building level, and lacks the means to evaluate and optimize the operating efficiency of multiple types of equipment within a single building. In addition, its optimization strategy generation is based on the solution of the objective function, and fails to introduce an equipment status scoring system or targeted maintenance recommendations, making it difficult to support energy-saving operations and hierarchical management of specific equipment. Summary of the Invention

[0005] In view of the fact that the existing building equipment energy consumption analysis methods generally have problems such as a single data collection dimension, rough energy efficiency evaluation methods, insufficient coupling between equipment operating status and environmental factors, and lack of effective hierarchical management and optimization feedback mechanisms, resulting in a lack of targetedness and low intelligence level in building energy efficiency management, which makes it difficult to meet the requirements of modern building energy conservation and refined operation and maintenance, the present invention is proposed.

[0006] Therefore, the problem to be solved by the present invention is how to construct an energy consumption benchmark model, quantitatively evaluate the energy efficiency deviation of equipment, and then realize hierarchical management based on energy efficiency scores, and generate optimization solutions for inefficient equipment to improve the operating efficiency of building equipment and the overall energy efficiency management level of the building.

[0007] In order to solve the above technical problems, the present invention provides the following technical solutions:

[0008] In a first aspect, the present invention provides a method for statistically analyzing and optimizing energy consumption of building equipment, which comprises:

[0009] Collecting and preprocessing energy consumption data of building equipment, wherein the energy consumption data includes equipment power consumption, operating time and environmental parameters;

[0010] The pre-processed energy consumption data is input into the constructed energy consumption benchmark model to obtain the energy efficiency deviation index;

[0011] Calculate the equipment energy efficiency score based on the energy efficiency deviation index and the environmental parameters;

[0012] Building equipment is managed in a hierarchical manner according to the equipment energy efficiency score. When the equipment energy efficiency score is lower than a preset threshold, an equipment maintenance warning is triggered and an equipment optimization plan is generated.

[0013] As a preferred solution of the building equipment energy consumption statistics and optimization method of the present invention, wherein: building equipment is hierarchically managed according to the energy efficiency score, and when the energy efficiency score is lower than a preset threshold, an equipment maintenance warning is triggered and an equipment optimization plan is generated, including:

[0014] Establish a building equipment grading management system, dividing all equipment in the building into different management levels based on the equipment energy efficiency score;

[0015] Build a dynamic monitoring platform for equipment energy efficiency to track the energy efficiency score trends of all equipment in real time. If the energy efficiency score of an equipment continues to decline and the decline exceeds the preset fluctuation range, the equipment will be marked as a risky device.

[0016] Based on different management levels, a multi-dimensional fault diagnosis algorithm is used to analyze the causes of reduced energy efficiency, and an equipment optimization plan is generated based on the diagnosis results.

[0017] As a preferred solution of the building equipment energy consumption statistics and optimization method of the present invention, the preset thresholds include a first threshold T1 and a second threshold T2; and differentiated management and control are performed based on the comparison results of the equipment energy efficiency score F with the preset thresholds, including:

[0018] When the equipment energy efficiency score F is greater than or equal to the second threshold T2, the building equipment is classified as a Class I energy efficiency grade device. If it is a Class I energy efficiency grade device, the device is marked as green, and the regular maintenance strategy is maintained. The operating status is tracked using the standard monitoring frequency, the normal control authority of the device is maintained, and only basic operating data is recorded.

[0019] When the equipment energy efficiency score F is greater than or equal to the first threshold T1 and less than the second threshold T2, the building equipment is classified as a Level 2 energy efficiency grade device. If it is a Level 2 energy efficiency grade device, the device is marked as yellow, a regular inspection mechanism is implemented, the monitoring frequency is increased, an energy efficiency reminder label is displayed on the device control interface, a reminder of equipment efficiency degradation is generated, and the equipment operating parameters are recorded and analyzed in detail.

[0020] When the equipment energy efficiency score F is less than the first threshold T1, the building equipment is a Class III energy efficiency grade equipment; if it is a Class III energy efficiency grade equipment, the equipment is marked in red status, included in the key monitoring list, the emergency response mechanism is activated, the equipment control authority is locked to maintenance mode, the equipment maintenance warning is triggered, an emergency work order is generated, and a multi-dimensional fault diagnosis algorithm is automatically executed to analyze the causes of energy efficiency reduction, and a phased optimization plan is formulated based on the diagnosis results.

[0021] As a preferred solution of the building equipment energy consumption statistics and optimization method described in the present invention, the preset threshold is based on the distribution characteristics of historical energy efficiency score data, and the dynamic grading threshold is set using the percentile method; the first threshold T1 is the 25% percentile of the historical energy efficiency score S; the second threshold T2 is the 50% percentile of the historical energy efficiency score S.

[0022] As a preferred solution of the building equipment energy consumption statistics and optimization method of the present invention, the energy efficiency score of the equipment is calculated based on the energy efficiency deviation index and combined with the environmental parameters, including:

[0023] Extracting energy efficiency deviation indicators of building equipment, grouping and classifying the energy efficiency deviation indicators according to equipment type, and generating an energy efficiency deviation matrix of equipment type;

[0024] Standardizing the environmental parameters and converting the values of the environmental parameters into environmental impact factors, wherein the environmental impact factors quantify the degree of influence of external conditions on the energy consumption of the equipment;

[0025] Based on the energy efficiency deviation matrix, a weighted calculation model is designed to determine the weight coefficients of environmental impact factors according to the sensitivity of different environmental parameters to the energy consumption of various types of equipment, and to generate environmental correction parameters;

[0026] Performing a composite calculation on the energy efficiency deviation index and the environmental correction parameter to eliminate the interference of the environmental parameter on the energy efficiency evaluation, and obtaining an environmental normalized energy efficiency deviation value;

[0027] An S-shaped nonlinear mapping function is used to convert the environmental normalized energy efficiency deviation value into an equipment energy efficiency score.

[0028] As a preferred solution of the building equipment energy consumption statistics and optimization method of the present invention, the pre-processed energy consumption data is input into the constructed energy consumption benchmark model to obtain the energy efficiency deviation index, including:

[0029] Collect historical energy consumption data of building equipment and use time series decomposition algorithm to decompose the historical energy consumption data into seasonal fluctuation components, long-term trend components and random fluctuation components to obtain the dimensional characteristics of energy consumption changes;

[0030] Based on the energy consumption change dimension characteristics, an energy consumption benchmark model is constructed using a multi-layer artificial neural network algorithm, wherein the multi-layer artificial neural network algorithm includes an input layer, a hidden layer and an output layer;

[0031] Dividing the historical energy consumption data into a training set and a validation set according to a preset ratio, and training and validating the energy consumption benchmark model;

[0032] Input the pre-processed energy consumption data into the verified energy consumption benchmark model and apply it to real-time energy consumption prediction to generate a standard energy consumption prediction value as the energy consumption benchmark value;

[0033] The real-time energy consumption value is compared with the energy consumption baseline value to calculate the energy efficiency deviation index.

[0034] As a preferred solution of the building equipment energy consumption statistics and optimization method described in the present invention, the input layer receives characteristic parameters, wherein the parameter characteristics include ambient temperature, equipment operating time and space utilization rate; the hidden layer uses the ReLU activation function to perform nonlinear feature extraction; and the output layer generates a standard energy consumption prediction value.

[0035] As a preferred solution of the method for statistics and optimization of building equipment energy consumption of the present invention, the current energy consumption data collected in real time is compared with the energy consumption benchmark, including:

[0036] When the relative deviation between the real-time energy consumption value and the energy consumption reference value is within the first threshold range, and the system operation stability time exceeds the first time threshold, the energy efficiency deviation index ΔE=(1-|E real -Ebase | / E base )×(1+T stable / T total ), where E real is the real-time energy consumption value, E base is the energy consumption benchmark value, T stable For stable operation time, T total is the total monitoring time;

[0037] When the relative deviation between the real-time energy consumption value and the energy consumption reference value exceeds the first threshold range but is lower than the second threshold range, or the system fluctuation time exceeds the second time threshold, the energy efficiency deviation index ΔE=θ×(1-|E real -E base | / E base )×(1-T fluc / T total )×(1-N abnormal / N total ), where θ is the first correction coefficient, T fluc is the fluctuation duration, N abnormal is the number of abnormal devices, N total is the total number of devices;

[0038] When the relative deviation between the real-time energy consumption value and the energy consumption reference value exceeds the second threshold range, or a sudden change in energy consumption occurs, the energy efficiency deviation index ΔE=λ×(1-|E real -E base | / E base )×(1-P peak / P rated )×(1-T abnormal / T total ), where λ is the second correction coefficient, P peak is the peak power, P rated is the rated power, T abnormal The abnormal running time.

[0039] In a second aspect, the present invention provides a computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: when the processor executes the computer program, it implements any step of the above-mentioned building equipment energy consumption statistics and optimization method.

[0040] In a third aspect, the present invention provides a computer-readable storage medium having a computer program stored thereon, wherein: when the computer program is executed by a processor, any step of the above-mentioned method for statistics and optimization of building equipment energy consumption is implemented.

[0041] Compared with the existing technology, the beneficial effects of the present invention are as follows: by collecting and preprocessing the energy consumption data of building equipment, combining the time series decomposition algorithm and the multi-layer artificial neural network, an energy consumption benchmark model is established, which can effectively identify the seasonal fluctuations, long-term trends and random fluctuation characteristics in the energy consumption data, improve the accuracy of energy consumption prediction, and provide a reliable data basis for energy efficiency evaluation; by introducing environmental impact factors and weight coefficients, an innovative energy efficiency scoring calculation method is designed, which not only takes into account the energy efficiency performance of the equipment itself, but also quantifies the influence of external environmental factors, and uses the S-shaped nonlinear mapping function for scoring conversion, so that the scoring results more objectively reflect the actual energy efficiency level of the equipment; by establishing dynamic grading thresholds and differentiated management and control mechanisms, intelligent hierarchical management of equipment is realized, and the management standards can be adaptively adjusted according to the distribution characteristics of historical data, and the corresponding level of maintenance warning can be triggered in time when the equipment energy efficiency is abnormal, thereby realizing preventive management of equipment energy efficiency problems. BRIEF DESCRIPTION OF THE DRAWINGS

[0042] To more clearly illustrate the technical solutions of the embodiments of the present invention, the following briefly introduces the drawings required for describing the embodiments. Obviously, the drawings described below are only some embodiments of the present invention. Those skilled in the art can also derive other drawings based on these drawings without inventive effort. Among them:

[0043] Figure 1 The figure is a flow chart of a method for statistics and optimization of energy consumption of building equipment. DETAILED DESCRIPTION

[0044] To make the above-mentioned objects, features, and advantages of the present invention more clearly understood, the following detailed description of the specific embodiments of the present invention is given in conjunction with the accompanying drawings. It is obvious that the described embodiments are only part of the embodiments of the present invention, but not all of them. Based on the embodiments of the present invention, all other embodiments obtained by ordinary persons in this field without creative work should fall within the scope of protection of the present invention.

[0045] In the following description, many specific details are set forth to facilitate a full understanding of the present invention. However, the present invention may also be implemented in other ways different from those described herein. Those skilled in the art may make similar generalizations without violating the connotation of the present invention. Therefore, the present invention is not limited to the specific embodiments disclosed below.

[0046] Secondly, the term "one embodiment" or "embodiment" herein refers to a specific feature, structure, or characteristic that may be included in at least one implementation of the present invention. The phrase "in one embodiment" appearing in various places throughout this specification does not necessarily refer to the same embodiment, nor does it refer to a separate or selective embodiment that is mutually exclusive of other embodiments.

[0047] The present invention is described in detail with reference to schematic diagrams. For ease of illustration, cross-sectional views of device structures may be partially enlarged and not to scale when describing embodiments of the present invention. Furthermore, the schematic diagrams are merely illustrative and should not limit the scope of the present invention. Furthermore, in actual production, the three-dimensional dimensions of length, width, and depth should be included.

[0048] In the description of the present invention, it should be noted that the terms "upper, lower, inner, and outer" and other references to orientations or positional relationships are based on the orientations or positional relationships shown in the accompanying drawings and are intended solely to facilitate and simplify the description of the present invention. They are not intended to indicate or imply that the devices or components referred to must have, be constructed, or operate in a specific orientation, and therefore should not be construed as limitations on the present invention. Furthermore, the terms "first, second, or third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0049] In this disclosure, unless otherwise specified or limited, the terms "mounted," "connected," and "connected" should be interpreted broadly. For example, they may refer to fixed, removable, or integral connections. They may also refer to mechanical, electrical, or direct connections, indirect connections through an intermediary, or internal communication between two components. Those skilled in the art will understand the specific meanings of these terms in this disclosure.

[0050] Example 1

[0051] Reference Figure 1 , which is the first embodiment of the present invention, provides a method for counting and optimizing energy consumption of building equipment, including:

[0052] S1: Collect and pre-process the energy consumption data of building equipment, including equipment power consumption, operating time and environmental parameters;

[0053] S2: Input the preprocessed energy consumption data into the constructed energy consumption benchmark model to obtain the energy efficiency deviation index;

[0054] S3: Calculate the equipment energy efficiency score based on the energy efficiency deviation index and environmental parameters;

[0055] S4: Building equipment is managed in a hierarchical manner according to the equipment energy efficiency score. When the equipment energy efficiency score is lower than the preset threshold, an equipment maintenance warning is triggered and an equipment optimization plan is generated.

[0056] In the embodiment of the present application, the above step S1 includes:

[0057] Specifically, smart meters are deployed in the power distribution circuits of building equipment to continuously collect equipment power consumption at preset time intervals (e.g., 15 minutes). Equipment power consumption includes active power, current, and voltage. At the same time, temperature and humidity sensors distributed on each floor of the building collect environmental parameters, including indoor temperature and relative humidity.

[0058] It should be noted that the smart meter and temperature and humidity sensor use RS-485 or LoRa communication protocols to transmit the collected data to the central data storage server in real time;

[0059] Furthermore, the energy consumption data is cleaned to remove outliers, missing values, and duplicate values. Specifically, outliers are identified and removed. The 3σ principle (triple standard deviation method) is used to detect the power consumption of the equipment. If the value at a certain moment exceeds the historical mean ±3 times the standard deviation range, it is determined to be an outlier and removed. For missing values, if a single piece of data is missing, linear interpolation of the adjacent data is used to fill it in. If multiple consecutive periods are missing, a multivariate regression model is established to predict and fill in the missing values by combining indoor temperature and humidity data. Duplicate data caused by communication delays or storage errors is checked and deleted.

[0060] It should be noted that the multivariate regression model is constructed based on the historical data of equipment power consumption and indoor temperature, relative humidity, operating time and time characteristics, and the energy consumption value of the missing period is predicted by fitting the linear relationship between the variables.

[0061] Furthermore, a moving average method is used to smooth the cleaned energy consumption data to reduce random noise interference. The specific method is: for the equipment power consumption series, the arithmetic mean is calculated according to a sliding window (such as 5 sampling points), and the original value of the window center point is replaced to make the data trend more stable while retaining key fluctuation characteristics.

[0062] Specifically, energy consumption data is categorized and organized by device type, floor distribution, and time period to construct a standardized energy consumption data matrix. This includes categorizing and organizing the smoothed energy consumption data by device type (e.g., air conditioning, lighting, elevator), floor distribution (e.g., 1F, 2F…NF), and time period (e.g., weekdays / holidays, peak / off-peak periods). Based on the categorization results, a standardized energy consumption data matrix is constructed, in which each row represents a device or area, and each column represents the device power consumption, indoor temperature, and humidity data at a point in time, forming a structured data set.

[0063] Furthermore, the energy consumption data matrix is normalized to eliminate the differences between different measurement units and obtain the preprocessed energy consumption data; the Min-Max normalization method is used to scale the equipment power consumption, indoor temperature and humidity to the [0,1] range respectively.

[0064] In the embodiment of the present application, the above step S2 includes:

[0065] Specifically, historical energy consumption data of building equipment was collected and decomposed into seasonal fluctuation components, long-term trend components, and random fluctuation components using a time series decomposition algorithm to obtain the dimensional characteristics of energy consumption changes. Based on these dimensional characteristics, a multi-layer artificial neural network algorithm was used to construct an energy consumption benchmark model.

[0066] It should be noted that the multi-layer artificial neural network algorithm includes an input layer, a hidden layer, and an output layer; the input layer receives feature parameters, where the parameter features include ambient temperature, equipment operating time, and space utilization rate; the hidden layer uses the ReLU activation function for nonlinear feature extraction; and the output layer generates a standard energy consumption prediction value.

[0067] Furthermore, the historical energy consumption data is divided into a training set and a validation set according to a preset ratio, and the energy consumption benchmark model is trained and validated; the pre-processed energy consumption data is input into the validated energy consumption benchmark model for real-time energy consumption prediction, and a standard energy consumption prediction value is generated as the energy consumption benchmark value;

[0068] Furthermore, the real-time energy consumption value is compared with the energy consumption benchmark value to calculate the energy efficiency deviation index; specifically, when the relative deviation between the real-time energy consumption value and the energy consumption benchmark value is within the first threshold range, and the system operation stable time exceeds the first time threshold, the energy efficiency deviation index ΔE=(1-|E real -E base | / E base )×(1+T stable / T total ), where E real is the real-time energy consumption value, E base is the energy consumption benchmark value, T stable For stable operation time, T total is the total monitoring time; when the relative deviation between the real-time energy consumption value and the energy consumption reference value exceeds the first threshold range but is lower than the second threshold range, or the system fluctuation time exceeds the second time threshold, the energy efficiency deviation index ΔE=θ×(1-|E real -E base | / E base )×(1-F fluc / T total )×(1-N abnormal / N total ), where θ is the first correction coefficient, T fluc is the fluctuation duration, N abnormal is the number of abnormal devices, N totalis the total number of devices; when the relative deviation between the real-time energy consumption value and the energy consumption benchmark value exceeds the second threshold range, or a sudden change in energy consumption occurs, the energy efficiency deviation index ΔE=λ×(1-|E real -E base | / E base )×(1-P peak / P rated )×(1-T abnormal / T total ), where λ is the second correction coefficient, P peak is the peak power, P rated is the rated power, T abnormal The abnormal running time.

[0069] In the embodiment of the present application, the above step S3 includes:

[0070] Specifically, the energy efficiency deviation indicators of building equipment are extracted, and the energy efficiency deviation indicators are grouped and classified according to equipment type to generate an energy efficiency deviation matrix for each equipment type. Environmental parameters are standardized and the numerical values in the environmental parameters are converted into environmental impact factors, where the environmental impact factors quantify the degree of influence of external conditions on equipment energy consumption. Based on the energy efficiency deviation matrix, a weighted calculation model is designed to determine the weight coefficients of the environmental impact factors according to the sensitivity of different environmental parameters to the energy consumption of various types of equipment, and generate environmental correction parameters.

[0071] Preferably, the weighted calculation model specifically includes: calculating the distribution of energy efficiency deviation indicators of each equipment type under different environmental impact factors to obtain an original data matrix; normalizing the original data matrix to eliminate dimensional differences; calculating the information entropy value corresponding to each environmental impact factor, wherein the information entropy value is equal to the sum of the product of the negative normalized data and its natural logarithm; calculating the entropy weight of each environmental impact factor based on the information entropy value, wherein the entropy weight is equal to one minus the information entropy value, divided by the number of environmental impact factors minus the sum of all information entropy values; and performing a weighted summation operation using the entropy weight and the environmental impact factor to generate an environmental correction parameter;

[0072] Furthermore, the energy efficiency deviation index is compounded with the environmental correction parameter to eliminate the interference of environmental parameters on the energy efficiency evaluation, and the environmental normalized energy efficiency deviation value is obtained; the environmental normalized energy efficiency deviation value is converted into the equipment energy efficiency score using an S-shaped nonlinear mapping function.

[0073] Furthermore, the specific formula for equipment energy efficiency scoring is as follows:

[0074]

[0075] Among them, F is the equipment energy efficiency score, k is the curve steepness adjustment coefficient, D is the environmental normalized energy efficiency deviation value, D0 is the energy efficiency deviation baseline value, ΔE is the energy efficiency deviation index, n is the number of environmental parameters, w i is the weight coefficient of the i-th environmental parameter, f i is the i-th environmental impact factor;

[0076] Specifically, according to the distribution characteristics of the equipment energy efficiency scores, the energy efficiency grade division threshold is set, and the building equipment is divided into grades to form the equipment energy efficiency grading results.

[0077] Furthermore, the preset thresholds include a first threshold T1 and a second threshold T2; the preset thresholds are based on the distribution characteristics of historical energy efficiency score data, and the dynamic grading thresholds are set using the percentile method; the first threshold T1 is the 25% percentile of the historical energy efficiency score S; the second threshold T2 is the 50% percentile of the historical energy efficiency score S.

[0078] Furthermore, when the equipment energy efficiency score F is greater than or equal to the second threshold T2, the building equipment is a first-level energy efficiency grade equipment; when the equipment energy efficiency score F is greater than or equal to the first threshold T1 and less than the second threshold T2, the building equipment is a second-level energy efficiency grade equipment; when the equipment energy efficiency score F is less than the first threshold T1, the building equipment is a third-level energy efficiency grade equipment.

[0079] As an example, during the implementation of graded energy efficiency ratings for building equipment at a certain building complex, energy efficiency rating data for various types of equipment over the past year was collected and aggregated to form a complete historical rating dataset. Percentile statistics were used to analyze the distribution characteristics of this dataset and set dynamic grading thresholds. After sorting the energy efficiency rating data in ascending order, the score corresponding to the 25th percentile was selected as the first threshold, T1, for example, 0.58; the score corresponding to the 50th percentile was selected as the second threshold, T2, for example, 0.72. The energy efficiency score F of each building equipment obtained through real-time monitoring is compared with T1 and T2. For example, if the energy efficiency score F of an air-conditioning system is 0.76, which is greater than T2, it is classified as a first-level energy efficiency grade equipment, indicating that its operating energy efficiency is excellent and no adjustment of the operating strategy is required. If the energy efficiency score of an elevator equipment is 0.65, which is between T1 and T2, it is classified as a second-level energy efficiency grade equipment, indicating that there is room for energy efficiency improvement. The energy efficiency score of another lighting system is only 0.51, which is lower than T1. It is classified as a third-level energy efficiency grade equipment and requires special attention and timely optimization of operating parameters or maintenance.

[0080] In the embodiment of the present application, the above step S4 includes:

[0081] Specifically, a building equipment grading management system will be established to classify all equipment in the building into different management levels based on the equipment energy efficiency scores;

[0082] Furthermore, based on the comparison results of the equipment energy efficiency score F and the preset threshold, differentiated management and control are implemented, including: if it is a first-level energy efficiency grade device, the device is marked as green, the regular maintenance strategy is maintained, the standard monitoring frequency is used to track the operating status, the normal control authority of the device is maintained, and only basic operating data is recorded; if it is a second-level energy efficiency grade device, the device is marked as yellow, a regular inspection mechanism is implemented, the monitoring frequency is increased, the energy efficiency prompt label is displayed on the device control interface, the equipment efficiency attenuation prompt is generated, and the equipment operating parameters are recorded and analyzed in detail; if it is a third-level energy efficiency grade device, the device is marked as red, included in the key monitoring list, the emergency response mechanism is activated, the equipment control authority is locked to the maintenance mode, the equipment maintenance warning is triggered, an emergency work order is generated, and a multi-dimensional fault diagnosis algorithm is automatically executed to analyze the cause of the energy efficiency reduction, and a phased optimization plan is formulated based on the diagnosis results.

[0083] Furthermore, a dynamic monitoring platform for equipment energy efficiency is built to track the changing trends of all equipment energy efficiency scores in real time. When it is monitored that the equipment energy efficiency score continues to decline and the decline exceeds the preset fluctuation range, the equipment will be marked as a risky equipment; based on different management levels, a multi-dimensional fault diagnosis algorithm is used to analyze the causes of energy efficiency reduction, and an equipment optimization plan is generated based on the diagnosis results.

[0084] Furthermore, the equipment optimization solution is applied to the building equipment control system, and the changes in energy consumption data before and after optimization are recorded to complete the statistics and optimization of building equipment energy consumption.

[0085] In summary, the present invention establishes an energy consumption benchmark model by collecting and preprocessing the energy consumption data of building equipment, combining time series decomposition algorithm and multi-layer artificial neural network. The model can effectively identify seasonal fluctuations, long-term trends and random fluctuation characteristics in energy consumption data, improve the accuracy of energy consumption prediction, and provide a reliable data basis for energy efficiency evaluation; by introducing environmental impact factors and weight coefficients, an innovative energy efficiency scoring calculation method is designed, which not only takes into account the energy efficiency performance of the equipment itself, but also quantifies the influence of external environmental factors, and uses S-shaped nonlinear mapping function for scoring conversion, so that the scoring results can more objectively reflect the actual energy efficiency level of the equipment; by establishing dynamic grading thresholds and differentiated management and control mechanisms, intelligent hierarchical management of equipment is realized, and management standards can be adaptively adjusted according to the distribution characteristics of historical data, and maintenance warnings of corresponding levels can be triggered in time when the equipment energy efficiency is abnormal, thereby realizing preventive management of equipment energy efficiency problems.

[0086] This embodiment also provides an electronic device, which includes a processor, memory, a communication interface, a display screen, and an input device connected via a system bus. The processor of the computer device is used to provide computing and control capabilities. The memory of the computer device includes a non-volatile storage medium and an internal memory. The non-volatile storage medium stores an operating system and a computer program. The internal memory provides an environment for the operation of the operating system and computer program in the non-volatile storage medium. The communication interface of the computer device is used to communicate with an external terminal in a wired or wireless manner. The wireless manner can be achieved through WIFI, an operator network, NFC (near field communication) or other technologies. When the computer program is executed by the processor, a multi-task edge computing resource scheduling method is implemented. The display screen of the computer device can be a liquid crystal display or an electronic ink display screen. The input device of the computer device can be a touch layer covering the display screen, or a button, trackball or touchpad provided on the computer device housing, or an external keyboard, touchpad or mouse.

[0087] This embodiment further provides a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the method proposed in the above embodiment is implemented.

[0088] The storage medium proposed in this embodiment and the method proposed in the above embodiment belong to the same inventive concept. For technical details not fully described in this embodiment, please refer to the above embodiment, and this embodiment has the same beneficial effects as the above embodiment.

[0089] Through the above description of the implementation methods, those skilled in the art can clearly understand that the present invention can be implemented with the help of software and necessary general-purpose hardware, and of course it can also be implemented by hardware, but in many cases the former is a better implementation method. Based on this understanding, the technical solution of the present invention is essentially or the part that contributes to the prior art can be embodied in the form of a software product, and the computer software product can be stored in a computer-readable storage medium, such as a computer floppy disk, read-only memory (ROM), random access memory (RAM), flash memory (FLASH), hard disk or optical disk, etc., including a number of instructions for enabling a computer device (which can be a personal computer, server, or network device, etc.) to execute the method of the embodiment of the present invention.

[0090] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit the present invention. Although the present invention has been described in detail with reference to the preferred embodiments, those skilled in the art should understand that the technical solutions of the present invention may be modified or replaced by equivalents without departing from the spirit and scope of the technical solutions of the present invention, which should all be included in the scope of the claims of the present invention.

[0091] Those skilled in the art will appreciate that the embodiments of the present application can be provided as methods, systems, or computer program products. Therefore, the application can adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the application can adopt the form of a computer program product implemented on one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that contain computer-usable program code. The scheme in the embodiment of the present application can be implemented in various computer languages.

[0092] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to produce a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the steps in the process. Figure 1 a process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0093] These computer program instructions may also be stored in a computer readable memory that can direct a computer or other programmable data processing device to work in a specific manner, so that the instructions stored in the computer readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 a process or multiple processes and / or boxes Figure 1 The function specified in one or more boxes.

[0094] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operational steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing the instructions executed on the computer or other programmable device for implementing the process. Figure 1 a process or multiple processes and / or boxes Figure 1 The steps for the function specified in one or more boxes.

[0095] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present application.

[0096] Obviously, those skilled in the art may make various changes and modifications to this application without departing from the spirit and scope of this application. Thus, if these modifications and variations of this application fall within the scope of the claims of this application and their equivalents, this application is intended to include these modifications and variations.

Claims

1. A method for statistics and optimization of energy consumption of building equipment, characterized by: include, Collecting and preprocessing energy consumption data of building equipment, wherein the energy consumption data includes equipment power consumption, operating time and environmental parameters; The pre-processed energy consumption data is input into the constructed energy consumption benchmark model to obtain the energy efficiency deviation index; Calculate the equipment energy efficiency score based on the energy efficiency deviation index and the environmental parameters; Building equipment is managed in a hierarchical manner according to the equipment energy efficiency score. When the equipment energy efficiency score is lower than a preset threshold, an equipment maintenance warning is triggered and an equipment optimization plan is generated.

2. The method for calculating and optimizing energy consumption of construction equipment according to claim 1, wherein: Building equipment is managed in a hierarchical manner according to the energy efficiency score. When the energy efficiency score falls below a preset threshold, an equipment maintenance warning is triggered and an equipment optimization plan is generated, including: Establish a building equipment grading management system, dividing all equipment in the building into different management levels based on the equipment energy efficiency score; Build a dynamic monitoring platform for equipment energy efficiency to track the energy efficiency score trends of all equipment in real time. If the energy efficiency score of an equipment continues to decline and the decline exceeds the preset fluctuation range, the equipment will be marked as a risky device. Based on different management levels, a multi-dimensional fault diagnosis algorithm is used to analyze the causes of reduced energy efficiency, and an equipment optimization plan is generated based on the diagnosis results.

3. The method for calculating and optimizing energy consumption of construction equipment according to claim 2, wherein: The preset thresholds include a first threshold T1 and a second threshold T2; and differentiated management and control are performed based on a comparison result between the equipment energy efficiency score F and the preset thresholds, including: When the equipment energy efficiency score F is greater than or equal to the second threshold T2, the building equipment is classified as a Class I energy efficiency grade device. If it is a Class I energy efficiency grade device, the device is marked as green, and the regular maintenance strategy is maintained. The operating status is tracked using the standard monitoring frequency, the normal control authority of the device is maintained, and only basic operating data is recorded. When the equipment energy efficiency score F is greater than or equal to the first threshold T1 and less than the second threshold T2, the building equipment is classified as a Level 2 energy efficiency grade device. If it is a Level 2 energy efficiency grade device, the device is marked as yellow, a regular inspection mechanism is implemented, the monitoring frequency is increased, an energy efficiency reminder label is displayed on the device control interface, a reminder of equipment efficiency degradation is generated, and the equipment operating parameters are recorded and analyzed in detail. When the equipment energy efficiency score F is less than the first threshold T1, the building equipment is a Class III energy efficiency grade equipment; if it is a Class III energy efficiency grade equipment, the equipment is marked in red status, included in the key monitoring list, the emergency response mechanism is activated, the equipment control authority is locked to maintenance mode, the equipment maintenance warning is triggered, an emergency work order is generated, and a multi-dimensional fault diagnosis algorithm is automatically executed to analyze the causes of energy efficiency reduction, and a phased optimization plan is formulated based on the diagnosis results.

4. The method for calculating and optimizing energy consumption of construction equipment according to claim 3, wherein: The preset threshold is based on the distribution characteristics of historical energy efficiency score data, and the dynamic classification threshold is set using the percentile method; the first threshold T1 is the 25% percentile of the historical energy efficiency score S; the second threshold T2 is the 50% percentile of the historical energy efficiency score S.

5. The method for calculating and optimizing energy consumption of construction equipment according to claim 4, wherein: Calculating the equipment energy efficiency score based on the energy efficiency deviation index and the environmental parameters includes: Extracting energy efficiency deviation indicators of building equipment, grouping and classifying the energy efficiency deviation indicators according to equipment type, and generating an energy efficiency deviation matrix of equipment type; Standardizing the environmental parameters and converting the values of the environmental parameters into environmental impact factors, wherein the environmental impact factors quantify the degree of influence of external conditions on the energy consumption of the equipment; Based on the energy efficiency deviation matrix, a weighted calculation model is designed to determine the weight coefficients of environmental impact factors according to the sensitivity of different environmental parameters to the energy consumption of various types of equipment, and to generate environmental correction parameters; Performing a composite calculation on the energy efficiency deviation index and the environmental correction parameter to eliminate the interference of the environmental parameter on the energy efficiency evaluation, and obtaining an environmental normalized energy efficiency deviation value; An S-shaped nonlinear mapping function is used to convert the environmental normalized energy efficiency deviation value into an equipment energy efficiency score.

6. The method for calculating and optimizing energy consumption of construction equipment according to claim 5, wherein: The pre-processed energy consumption data is input into the constructed energy consumption benchmark model to obtain energy efficiency deviation indicators, including: Collect historical energy consumption data of building equipment and use time series decomposition algorithm to decompose the historical energy consumption data into seasonal fluctuation components, long-term trend components and random fluctuation components to obtain the dimensional characteristics of energy consumption changes; Based on the energy consumption change dimension characteristics, an energy consumption benchmark model is constructed using a multi-layer artificial neural network algorithm, wherein the multi-layer artificial neural network algorithm includes an input layer, a hidden layer and an output layer; Dividing the historical energy consumption data into a training set and a validation set according to a preset ratio, and training and validating the energy consumption benchmark model; Input the pre-processed energy consumption data into the verified energy consumption benchmark model and apply it to real-time energy consumption prediction to generate a standard energy consumption prediction value as the energy consumption benchmark value; The real-time energy consumption value is compared with the energy consumption baseline value to calculate the energy efficiency deviation index.

7. The method for calculating and optimizing energy consumption of construction equipment according to claim 6, wherein: The input layer receives feature parameters, wherein the parameter features include ambient temperature, equipment operating time and space usage rate; the hidden layer uses the ReLU activation function to perform nonlinear feature extraction; and the output layer generates a standard energy consumption prediction value.

8. The method for calculating and optimizing energy consumption of construction equipment according to claim 6, wherein: Compare the current energy consumption data collected in real time with the energy consumption baseline, including: When the relative deviation between the real-time energy consumption value and the energy consumption reference value is within the first threshold range, and the system operation stability time exceeds the first time threshold, the energy efficiency deviation index ΔE=(1-|E real -E base | / E base )×(1+T stable / T total ), where E real is the real-time energy consumption value, E base is the energy consumption benchmark value, T stable For stable operation time, T total is the total monitoring time; When the relative deviation between the real-time energy consumption value and the energy consumption reference value exceeds the first threshold range but is lower than the second threshold range, or the system fluctuation time exceeds the second time threshold, the energy efficiency deviation index ΔE=θ×(1-|E real -E base | / E base )×(1-T fluc / T total )×(1-N abnormal / N total ), where θ is the first correction coefficient, T fluc is the fluctuation duration, N abnormal is the number of abnormal devices, N total is the total number of devices; When the relative deviation between the real-time energy consumption value and the energy consumption reference value exceeds the second threshold range, or a sudden change in energy consumption occurs, the energy efficiency deviation index ΔE=λ×(1-|E real -E base | / E base )×(1-P peak / P rated )×(1-T abnormal / T total ), where λ is the second correction coefficient, P peak is the peak power, P rated is the rated power, T abnormal The abnormal running time.

9. A computer device comprising a memory and a processor, wherein the memory stores a computer program, wherein: When the processor executes the computer program, the steps of the building equipment energy consumption statistics and optimization method according to any one of claims 1 to 8 are implemented.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the building equipment energy consumption statistics and optimization method according to any one of claims 1 to 8 are implemented.

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