CN117669860A - Electrical equipment energy efficiency evaluation method, device, equipment and medium - Google Patents
Electrical equipment energy efficiency evaluation method, device, equipment and medium Download PDFInfo
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Abstract
The application discloses an electrical equipment energy efficiency evaluation method, a device, equipment and a medium, relates to the technical field of equipment energy efficiency evaluation, and comprises the following steps: acquiring original equipment data of real-time equipment data, nameplate information and equipment installation information of each piece of electrical equipment, comparing the original equipment data with standard nameplate information and standard equipment installation information, and screening target electrical equipment; inputting real-time equipment data of the target electrical equipment into a preset energy efficiency evaluation model so that the preset energy efficiency evaluation model can acquire an energy efficiency evaluation result of the target electrical equipment by analyzing and comparing the real-time equipment data with standard equipment energy efficiency data of a corresponding use stage of the target electrical equipment; and generating an energy-saving report of the target electrical equipment based on the energy efficiency evaluation result. And carrying out multi-dimensional and multi-angle energy efficiency evaluation on different using stages of the electrical equipment by combining a mode of static evaluation with dynamic evaluation on the acquired original equipment data of each electrical equipment, and generating a corresponding energy saving report.
Description
Technical Field
The invention relates to the technical field of equipment energy efficiency evaluation, in particular to an electrical equipment energy efficiency evaluation method, an electrical equipment energy efficiency evaluation device, an electrical equipment energy efficiency evaluation equipment and an electrical equipment energy efficiency evaluation medium.
Background
The current management efficiency of high-energy consumption electric equipment of enterprises is low, the main method for managing the electric equipment mainly adopts an empirical method and a manual operation observation method, the two methods highly depend on the level and experience of management staff, a digital and intelligent management means is not provided, and because the equipment is not monitored in real time, omission is easy to occur, and management loss is caused; the traditional modes such as manual transcription, paper form recording and the like are adopted, so that the efficiency is low, the equipment data information cannot be comprehensively and systematically mastered, scientific decisions are difficult to make, and the traceability is difficult; in addition, depending on experience, personnel loss or misjudgment can cause non-ideal transformation effect and deviation from design expectation. In the prior art, static data or a single method is adopted for evaluating the energy efficiency of equipment, so that a conclusion is that a current evaluation section can be obtained, only an evaluation result at the moment can be formed, the accuracy is not high, and meanwhile, the static method cannot learn autonomously and cannot establish a complete full life cycle evaluation system.
In summary, how to comprehensively and automatically analyze different equipment indexes in static state and different equipment indexes in dynamic state for the electric equipment so as to realize multi-dimensional and multi-angle energy efficiency evaluation of electric equipment, and obtain accurate evaluation results is a technical problem to be solved in the field.
Disclosure of Invention
Accordingly, the invention aims to provide an electrical equipment energy efficiency evaluation method, an electrical equipment energy efficiency evaluation device, electrical equipment energy efficiency evaluation equipment and an electrical equipment energy efficiency evaluation medium, which can comprehensively and automatically analyze different equipment indexes in static state and different equipment indexes in dynamic state of electrical equipment so as to realize multi-dimensional and multi-angle energy efficiency evaluation of electric equipment and obtain accurate evaluation results. The specific scheme is as follows:
in a first aspect, the present application discloses an electrical device energy efficiency assessment method, comprising:
acquiring original equipment data of real-time equipment data, nameplate information and equipment installation information of each piece of electrical equipment, and comparing the original equipment data with standard nameplate information and standard equipment installation information to screen target electrical equipment;
inputting the real-time equipment data of the target electrical equipment into a preset energy efficiency evaluation model so that the preset energy efficiency evaluation model can obtain an energy efficiency evaluation result of the target electrical equipment by analyzing and comparing the real-time equipment data with standard equipment energy efficiency data of a use stage corresponding to the target electrical equipment;
and generating an energy saving report of the target electrical equipment based on the energy efficiency evaluation result.
Optionally, the comparing the original equipment data with standard nameplate information and standard equipment installation information to screen the target electrical equipment includes:
and comparing the nameplate information and the equipment installation information of the electrical equipment in the original equipment data of each electrical equipment with standard nameplate information and standard equipment installation information in a preset equipment index library respectively so as to screen out non-scrapped electrical equipment as target electrical equipment.
Optionally, before inputting the real-time device data of the target electrical device into a preset energy efficiency evaluation model, the method further includes:
respectively establishing an energy efficiency index weight model for outputting an energy efficiency index weight proportion corresponding to real-time equipment data of each electric equipment, an equipment life cycle segmentation model for outputting an energy efficiency change condition corresponding to the life cycle of each electric equipment, a marker post evaluation model for comparing the energy consumption change condition of similar electric equipment and an energy saving energy prediction model for predicting corresponding energy efficiency after replacing the current electric equipment with the similar electric equipment;
and constructing a preset energy efficiency evaluation model based on the energy efficiency index weight model, the equipment life cycle segmentation model, the marker post evaluation model and the energy saving prediction model.
Optionally, after the preset energy efficiency evaluation model is constructed based on the energy efficiency index weight model, the equipment life cycle segmentation model, the marker post evaluation model and the energy saving prediction model, the method further includes:
performing model correction on the preset energy efficiency evaluation model by using expert knowledge and a preset time recurrent neural network to obtain a corrected energy efficiency evaluation model;
correspondingly, the inputting the real-time equipment data of the target electrical equipment into a preset energy efficiency evaluation model so that the preset energy efficiency evaluation model can analyze and compare the real-time equipment data with standard equipment energy efficiency data of a using stage corresponding to the target electrical equipment, and the method comprises the following steps:
and inputting the real-time equipment data of the target electrical equipment into the corrected energy efficiency evaluation model, so as to analyze and compare the real-time equipment data with the standard equipment energy efficiency data of the corresponding use stage of the target electrical equipment through the corrected energy efficiency evaluation model.
Optionally, the analyzing and comparing, by the modified energy efficiency evaluation model, the real-time device data with standard device energy efficiency data of a usage stage corresponding to the target electrical device includes:
Determining target energy efficiency data of the target electrical equipment based on the real-time equipment data through the corrected energy efficiency evaluation model;
and comparing and analyzing the target energy efficiency data with the standard equipment energy efficiency data of the corresponding use stage through the target pole evaluation model in the corrected energy efficiency evaluation model to obtain an energy efficiency evaluation result of the target electrical equipment.
Optionally, the determining, by the modified energy efficiency evaluation model, target energy efficiency data of the target electrical device based on the real-time device data includes:
extracting the equipment characteristic quantity of the real-time equipment data through the energy efficiency index weight model in the corrected energy efficiency evaluation model, determining a target object from the equipment characteristic quantity, searching the nearest neighbors of the target object, and calculating and outputting the energy efficiency index weight proportion of the target electrical equipment based on the distance between the nearest neighbors; the device characteristic quantity is a numerical value or a numerical vector describing a device characteristic of the target electrical device;
and determining a current use stage of the target electrical equipment based on the real-time equipment data through the equipment life cycle segmentation model in the corrected energy efficiency evaluation model, and calculating target energy efficiency data of the target electrical equipment according to working condition information corresponding to the current use stage and the energy efficiency index weight proportion corresponding to the target electrical equipment.
Optionally, the electrical device energy efficiency evaluation method further includes:
counting the energy efficiency evaluation results of the similar electrical devices, and taking a target electrical device corresponding to the lowest device energy consumption in the energy efficiency evaluation results as a marker post electrical device;
and respectively storing nameplate information and equipment installation information of the standard post electrical equipment as standard nameplate information and standard equipment installation information in a preset equipment index library.
In a second aspect, the present application discloses an electrical device energy efficiency assessment apparatus comprising:
the device screening module is used for acquiring original device data of each electrical device, which comprises real-time device data, nameplate information and device installation information, and comparing the original device data with standard nameplate information and standard device installation information to screen target electrical devices;
the energy efficiency evaluation module is used for inputting the real-time equipment data of the target electrical equipment into a preset energy efficiency evaluation model so that the preset energy efficiency evaluation model can acquire an energy efficiency evaluation result of the target electrical equipment by analyzing and comparing the real-time equipment data with standard equipment energy efficiency data of a corresponding use stage of the target electrical equipment;
And the report generation module is used for generating an energy-saving report of the target electrical equipment based on the energy efficiency evaluation result.
In a third aspect, the present application discloses an electronic device comprising:
a memory for storing a computer program;
a processor for executing the computer program to implement the steps of the previously disclosed electrical device energy efficiency assessment method.
In a fourth aspect, the present application discloses a computer-readable storage medium for storing a computer program; wherein the computer program when executed by a processor implements the steps of the electrical device energy efficiency assessment method disclosed previously.
As can be seen, the present application discloses an electrical device energy efficiency evaluation method, comprising: acquiring original equipment data of real-time equipment data, nameplate information and equipment installation information of each piece of electrical equipment, and comparing the original equipment data with standard nameplate information and standard equipment installation information to screen target electrical equipment; inputting the real-time equipment data of the target electrical equipment into a preset energy efficiency evaluation model so that the preset energy efficiency evaluation model can obtain an energy efficiency evaluation result of the target electrical equipment by analyzing and comparing the real-time equipment data with standard equipment energy efficiency data of a use stage corresponding to the target electrical equipment; and generating an energy saving report of the target electrical equipment based on the energy efficiency evaluation result. Therefore, through a static evaluation mode of the acquired original equipment data of each electric equipment, primary evaluation is carried out to screen out target electric equipment, then the real-time equipment data of the screened target electric equipment is input into a preset energy efficiency evaluation model, the real-time equipment data and the standard equipment energy efficiency data of the corresponding stage are analyzed and compared through the preset energy efficiency evaluation model, dynamic energy efficiency evaluation of each equipment index of the target electric equipment is realized, the energy efficiency evaluation result of the whole life cycle of the whole electric equipment is not a fixed evaluation result, multi-dimensional multi-angle energy efficiency evaluation is carried out on different use stages of the electric equipment, and after the energy efficiency evaluation result is acquired, energy saving advice is further carried out according to the energy efficiency evaluation result, so that a corresponding energy saving report is generated.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings that are required to be used in the embodiments or the description of the prior art will be briefly described below, and it is obvious that the drawings in the following description are only embodiments of the present invention, and that other drawings can be obtained according to the provided drawings without inventive effort for a person skilled in the art.
FIG. 1 is a flow chart of an electrical device energy efficiency assessment method disclosed herein;
FIG. 2 is a flow chart of a specific electrical device energy efficiency assessment method disclosed herein;
FIG. 3 is a flow chart of a method for evaluating the energy efficiency AI of a device as disclosed herein;
FIG. 4 is a schematic structural diagram of an electrical device energy efficiency evaluation apparatus disclosed in the present application;
fig. 5 is a block diagram of an electronic device disclosed in the present application.
Detailed Description
The following description of the technical solutions in the embodiments of the present application will be made clearly and completely with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are only some embodiments of the present invention, not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The current management efficiency of high-energy consumption electric equipment of enterprises is low, the main method for managing the electric equipment mainly adopts an empirical method and a manual operation observation method, the two methods highly depend on the level and experience of management staff, a digital and intelligent management means is not provided, and because the equipment is not monitored in real time, omission is easy to occur, and management loss is caused; the traditional modes such as manual transcription, paper form recording and the like are adopted, so that the efficiency is low, the equipment data information cannot be comprehensively and systematically mastered, scientific decisions are difficult to make, and the traceability is difficult; in addition, depending on experience, personnel loss or misjudgment can cause non-ideal transformation effect and deviation from design expectation. In the prior art, static data or a single method is adopted for evaluating the energy efficiency of equipment, so that a conclusion is that a current evaluation section can be obtained, only an evaluation result at the moment can be formed, the accuracy is not high, and meanwhile, the static method cannot learn autonomously and cannot establish a complete full life cycle evaluation system.
Therefore, the invention provides an electrical equipment energy efficiency evaluation scheme which can comprehensively and automatically analyze different equipment indexes in static state and different equipment indexes in dynamic state of electrical equipment so as to realize multi-dimensional and multi-angle energy efficiency evaluation of electric equipment and obtain accurate evaluation results.
Referring to fig. 1, an embodiment of the invention discloses an electrical device energy efficiency evaluation method, which includes:
step S11: original equipment data of real-time equipment data, nameplate information and equipment installation information of each electrical equipment are obtained, and the original equipment data are compared with standard nameplate information and standard equipment installation information to screen target electrical equipment.
In this embodiment, raw device data of each electrical device is obtained, where the electrical devices may specifically include, but are not limited to: water pump, fan, transformer, air conditioner, heat pump, boiler, etc. The original equipment data may specifically include: real-time device data, nameplate information, and device installation information, wherein the real-time device data may specifically include, but is not limited to: device power, device voltage, device current, power factor, frequency, temperature, flow rate, rotational speed, etc. The nameplate information can include, in particular, but is not limited to: manufacturer, equipment model, equipment capacity, etc., the equipment installation information may specifically include, but is not limited to: design drawings, installation time, location, design capacity, load, etc.
In this embodiment, the nameplate information, the device installation information of the electrical devices in the original device data of each electrical device are compared with standard nameplate information and standard device installation information in a preset device index library, so as to screen out non-scrapped electrical devices as target electrical devices. It will be appreciated that the raw equipment data obtained is compared with standard nameplate information and standard equipment installation information. Specifically, the manufacturer and equipment model information in the original equipment data of each piece of electric equipment are compared with the equipment elimination catalog in the standard nameplate information, whether the original equipment data are on the equipment elimination catalog is judged, if yes, the corresponding piece of electric equipment is judged to be the scrapped electric equipment, the energy efficiency assessment flow is ended, and the energy efficiency result of the scrapped electric equipment does not need to be further assessed. If not, the corresponding electrical equipment is judged to be non-scrapped equipment, and information such as equipment energy efficiency grade, area, voltage grade, manufacturer, model, year and the like in the original data of the target electrical equipment are further compared with standard nameplate information and standard equipment installation information respectively, so that static evaluation is realized, and a static evaluation standard analysis result and the target electrical equipment are obtained.
Step S12: and inputting the real-time equipment data of the target electrical equipment into a preset energy efficiency evaluation model so that the preset energy efficiency evaluation model can obtain an energy efficiency evaluation result of the target electrical equipment by analyzing and comparing the real-time equipment data with standard equipment energy efficiency data of a use stage corresponding to the target electrical equipment.
In this embodiment, the real-time device data of the target electrical device is input to a preset energy efficiency evaluation model, and the real-time device data is calculated by the preset energy efficiency evaluation model to obtain calculated actual energy efficiency data, that is, target energy efficiency data of the target electrical device. And then comparing and analyzing the actual energy efficiency data with the standard equipment energy efficiency data of the current use stage of the target electrical equipment through a preset energy efficiency evaluation model, and obtaining an energy efficiency evaluation result of the target electrical equipment after the comparison and analysis.
In this embodiment, before inputting the real-time device data of the target electrical device to the preset energy efficiency evaluation model, the method further includes: respectively establishing an energy efficiency index weight model for outputting an energy efficiency index weight proportion corresponding to real-time equipment data of each electric equipment, an equipment life cycle segmentation model for outputting an energy efficiency change condition corresponding to the life cycle of each electric equipment, a marker post evaluation model for comparing the energy consumption change condition of similar electric equipment and an energy saving energy prediction model for predicting corresponding energy efficiency after replacing the current electric equipment with the similar electric equipment; and constructing a preset energy efficiency evaluation model based on the energy efficiency index weight model, the equipment life cycle segmentation model, the marker post evaluation model and the energy saving prediction model. It can be understood that an energy efficiency index weight model for outputting the energy efficiency index weight proportion corresponding to the real-time equipment data of each electric equipment is established, the weight proportion corresponding to each energy efficiency index of each electric equipment can be respectively output according to the real-time equipment data of different electric equipment through the energy efficiency index weight model, the situation that all electric equipment use the same and fixed weight proportion is avoided, and the weight proportion coefficient corresponding to each electric equipment is generated. The method comprises the steps of establishing an equipment life cycle segmentation model for outputting energy efficiency change conditions corresponding to life cycles of all electrical equipment, and outputting the energy efficiency change conditions corresponding to current use stages in the life cycles of all electrical equipment, namely actual energy efficiency data, through the equipment life cycle segmentation model according to real-time equipment data of different electrical equipment. The method comprises the steps of establishing a marker post evaluation model for comparing the energy consumption change conditions of similar electrical equipment, comparing actual energy efficiency data of different electrical equipment and respective historical similar electrical equipment energy consumption changes through the marker post evaluation model, and determining the electrical equipment with optimal energy efficiency in the similar electrical equipment. And establishing an energy-saving prediction model for predicting corresponding energy efficiency after replacing the current electrical equipment with the similar electrical equipment. And then constructing a preset energy efficiency evaluation model based on the energy efficiency index weight model, the equipment life cycle segmentation model, the marker post evaluation model and the energy conservation quantity prediction model.
In this embodiment, the energy efficiency evaluation results of the similar electrical devices are counted, and a target electrical device corresponding to the lowest device energy consumption in the energy efficiency evaluation results is used as a marker post electrical device; and respectively storing nameplate information and equipment installation information of the standard post electrical equipment as standard nameplate information and standard equipment installation information in a preset equipment index library. It can be understood that the energy efficiency evaluation ranking of the target electrical equipment in the similar electrical equipment can be performed through the marker post evaluation model, then the electrical equipment corresponding to the lowest equipment energy consumption in the similar electrical equipment is determined according to the energy efficiency evaluation ranking, the electrical equipment is used as the marker post electrical equipment, the nameplate information and the equipment installation information of the marker post electrical equipment in each type of electrical equipment are respectively used as standard nameplate information and standard equipment installation information and are stored in the preset equipment index library for updating the standard equipment index information in the preset equipment index library, and the standard equipment index information in the preset equipment index library is generally the equipment index information specified in the industry before the marker post evaluation model does not output the marker post electrical equipment and the actual energy efficiency evaluation data thereof.
Step S13: and generating an energy saving report of the target electrical equipment based on the energy efficiency evaluation result.
In this embodiment, after the energy efficiency evaluation result of the target electrical device is output, the cost change is analyzed in combination with the cost evaluation, and the loss and cost saving conditions are estimated; and the strategy proposal and the energy-saving analysis provide a professional operation and maintenance optimization strategy, a processing proposal and an energy-saving evaluation month analysis report, and provide decision support for users.
As can be seen, the present application discloses an electrical device energy efficiency evaluation method, comprising: acquiring original equipment data of each electrical equipment, wherein the original equipment data comprises real-time equipment data, nameplate information and equipment installation information, and comparing the original equipment data with standard nameplate information and standard equipment installation information to screen target electrical equipment; inputting the real-time equipment data of the target electrical equipment into a preset energy efficiency evaluation model so that the preset energy efficiency evaluation model can obtain an energy efficiency evaluation result of the target electrical equipment by analyzing and comparing the real-time equipment data with standard equipment energy efficiency data of a use stage corresponding to the target electrical equipment; and generating an energy saving report of the target electrical equipment based on the energy efficiency evaluation result. Therefore, through a static evaluation mode of the acquired original equipment data of each electric equipment, primary evaluation is carried out to screen out target electric equipment, then the real-time equipment data of the screened target electric equipment is input into a preset energy efficiency evaluation model, the real-time equipment data and the standard equipment energy efficiency data of the corresponding stage are analyzed and compared through the preset energy efficiency evaluation model, dynamic energy efficiency evaluation of each equipment index of the target electric equipment is realized, the energy efficiency evaluation result of the whole life cycle of the whole electric equipment is not a fixed evaluation result, multi-dimensional multi-angle energy efficiency evaluation is carried out on different use stages of the electric equipment, and after the energy efficiency evaluation result is acquired, energy saving advice is further carried out according to the energy efficiency evaluation result, so that a corresponding energy saving report is generated.
Referring to fig. 2, an embodiment of the present invention discloses a specific method for evaluating energy efficiency of an electrical device, and compared with the previous embodiment, the present embodiment further describes and optimizes a technical solution. Specific:
step S21: constructing a preset energy efficiency evaluation model based on the energy efficiency index weight model, the equipment life cycle segmentation model, the marker post evaluation model and the energy conservation quantity prediction model; and carrying out model correction on the preset energy efficiency evaluation model by using expert knowledge and a preset time recurrent neural network so as to obtain a corrected energy efficiency evaluation model.
In this embodiment, a preset energy efficiency evaluation model is constructed based on an energy efficiency index weight model, an equipment life cycle segmentation model, a marker post evaluation model and an energy saving prediction model, and then coupling analysis is performed on original equipment data of energy and electrical equipment by combining AI deep learning, specifically, model correction is performed on the preset energy efficiency evaluation model by using expert knowledge and a preset time recurrent neural network, for example: storing a preset energy efficiency evaluation model into an expert database, performing deep learning according to model data and a Long Short-Term Memory (LSTM) algorithm, dividing equipment acquisition data into training set data and test set data based on the LSTM algorithm, performing model evaluation, and measuring error by standard deviation according to the obtained model. Wherein, the device energy efficiency balance scoring rules are as follows:
D n ＝ω(t)×k 1 +S×k 2 +PUE×k 3 +MGN(1,N)×k 4 ；
Wherein D is n Representing the energy efficiency balance score, k, of the nth electrical device 1 Scoring coefficients, k, for energy efficiency index weight model 2 Scoring coefficients, k, for a segmented model of a device lifecycle 3 Evaluating model scoring coefficients, k, for benchmarks 4 And (3) scoring coefficients for the energy-saving prediction model, wherein the coefficients can be manually preset by an expert and corrected and referenced through a neural network deep learning algorithm, ω (t) represents the energy efficiency index weight proportion of the electrical equipment, S represents the energy efficiency change condition of the electrical equipment, PUE represents the energy consumption change condition of the electrical equipment output after comparison with the similar electrical equipment, and MGN (1, N) represents the energy efficiency change condition based on the electrical equipment for predicting the future energy efficiency change condition.
Step S22: and inputting the real-time equipment data of the target electrical equipment into the corrected energy efficiency evaluation model, so as to analyze and compare the real-time equipment data with the standard equipment energy efficiency data of the use stage corresponding to the target electrical equipment through the corrected energy efficiency evaluation model, and obtain an energy efficiency evaluation result of the target electrical equipment.
In this embodiment, the real-time device data of the target electrical device is input to the corrected energy efficiency evaluation model, so that the standard device energy efficiency data of the use stage corresponding to the real-time device data and the target electrical device are analyzed and compared respectively through the energy efficiency index weight model, the device life cycle segmentation model, the marker post evaluation model and the energy saving energy prediction model in the corrected energy efficiency evaluation model, and the specific analysis and comparison process is as follows: determining target energy efficiency data of the target electrical equipment based on the real-time equipment data through the corrected energy efficiency evaluation model; and comparing and analyzing the target energy efficiency data with the standard equipment energy efficiency data of the corresponding use stage through the target pole evaluation model in the corrected energy efficiency evaluation model to obtain an energy efficiency evaluation result of the target electrical equipment. It can be understood that the target energy efficiency data of the target electrical equipment is determined based on the real-time equipment data, and then the target energy efficiency data and the standard equipment energy efficiency data of the corresponding use stage are compared and analyzed through the marker post evaluation model in the corrected energy efficiency evaluation model, so that the energy efficiency evaluation result of the target electrical equipment is obtained.
In this embodiment, the determining, by the modified energy efficiency evaluation model, the target energy efficiency data of the target electrical device based on the real-time device data includes: extracting the equipment characteristic quantity of the real-time equipment data through the energy efficiency index weight model in the corrected energy efficiency evaluation model, determining a target object from the equipment characteristic quantity, searching the nearest neighbors of the target object, and calculating and outputting the energy efficiency index weight proportion of the target electrical equipment based on the distance between the nearest neighbors; the device characteristic quantity is a numerical value or a numerical vector describing a device characteristic of the target electrical device; and determining a current use stage of the target electrical equipment based on the real-time equipment data through the equipment life cycle segmentation model in the corrected energy efficiency evaluation model, and calculating target energy efficiency data of the target electrical equipment according to working condition information corresponding to the current use stage and the energy efficiency index weight proportion corresponding to the target electrical equipment.
(1) Energy efficiency index weight model:
extracting equipment characteristic quantity of real-time equipment data through an energy efficiency index weight model as an analysis object of the energy efficiency index weight model, wherein the energy efficiency index weight model is based on a discrete outlier matrix, and for each weight factor t, k nearest neighbors of an object o are found from the discrete outlier matrix and recorded as f 1 (o),...,f k (o). Where k is an application dependent parameter. The weight of the weight t is defined as follows:
wherein dist (f) 1 ,f i (t)) is expressed as the distance from the 1 st nearest neighbor to the i-th nearest neighbor, which may be euclidean distance, manhattan distance, or other suitable distance measure. This weight reflects the degree of influence of the weight factor t on the target object o, or alternatively, the outlier nature of t in determining oImportance in quality. In general, the purpose of this formula is to determine the importance of the weight factor t by taking into account the nearest neighbors of the object o in order to better analyze the outlier properties of the object.
The device feature quantity refers to a numerical value or a numerical vector describing a device characteristic or feature. These characteristics may include physical properties of the device, such as size, weight, color, power, etc., as well as functional characteristics of the device, such as processing power, storage capacity, network connection speed, etc. The device feature quantity is important for the identification, classification and evaluation of the device. In machine learning and data mining, device feature quantities may be used to build models to predict features in terms of device performance, reliability, and applicability. For example, in device failure prediction, a model may be constructed using characteristic quantities of temperature, voltage, current, etc. of the device to predict whether the device will fail.
(2) Device lifecycle segmentation model:
the calculation mode of the equipment life cycle segmentation model adopts a segmentation balance method, and mainly considers that the working condition of the electrical equipment changes in different use stages. The calculation energy efficiency formula of the equipment life cycle segmentation model is as follows:
wherein S is the comprehensive energy efficiency of the whole life cycle,fitting energy efficiency condition for N sections, +.>Representing the intrinsic efficiency ratio of the target electrical device, Q PUE Indicating a target operating efficiency ratio of the target electrical device.
(3) Marker post assessment model:
η (Dev) ＝E out /E in ；
wherein eta is (Dev) Representing the energy efficiency of the target electrical equipment, E out Representing the output energy of the target electrical device, E in Representing the target electrical device input energy.
Q PUE ＝∑ P ×k/∑ PE ×η (Dev) ；
In which Q PUE Representing the inherent efficiency ratio of the device, also the nameplate efficiency, Σ of the target electrical device P The process load of the electrical equipment which is expressed as a design target is represented, k is a coefficient, and the value range is (0-1); sigma (sigma) PE Expressed as comprehensive energy, eta (Dev) Represented as static estimated energy efficiency.
Q PUE(t) ＝∑ P(t) /∑ PE ×η (Devt) ；
In which Q PUE(t) Actual operating efficiency ratio of the plant, Σ P(t) Expressed as the actual average load per unit time, eta (Devt) Expressed as average device efficiency per unit time. It should be noted that the efficiency of the target electrical device includes an inherent efficiency ratio and an actual operating efficiency ratio.
Ranking the similar devices through the benchmarking model optimizes the device energy efficiency definition as a benchmarking.
(4) Energy conservation quantity prediction model:
the energy-saving prediction model adopts an MGN (1, N) multivariable first-order gray differential equation model, and uses real-time equipment data of target electrical equipment to model, and the reliability of the verification model is recovered through back generation during prediction.
X -(0) (t)＝X -(1) (t)-X -(1) (t-1)；
In the formula, a series of prediction basis formulas, X -(0) In order to set the system as a characteristic gray system, a data matrix is constructed by least square fitting, and a sequence is established.
Step S23: and generating an energy saving report of the target electrical equipment based on the energy efficiency evaluation result.
The more detailed processing in step S23 is referred to the above disclosure, and is not performed here.
Referring to fig. 3, a device energy efficiency AI evaluation method is disclosed, specifically, 1, a data source: the equipment to be evaluated is selected, main research objects are a water pump, a fan, a transformer, an air conditioner, a heat pump, a boiler and the like, real-time data, nameplate information and basic information of each equipment are collected, the real-time data comprise power, voltage, current, power factor, frequency, temperature, flow speed, rotating speed and the like, the nameplate information comprises manufacturers, models, capacities and the like, the basic information comprises design drawings, installation time, positions, design capacities, loads and the like, and massive basic data collection is achieved.
2. Target data: and cleaning the source data, determining the data analysis direction, performing static evaluation and benchmarking analysis on the primary screening by adopting a static evaluation method according to equipment elimination catalogue, equipment energy efficiency level, area, voltage level, manufacturer, model and year, and establishing a basic index library for benchmarking.
3. Model evaluation: analyzing by using the established influence weight model, life cycle segmentation model, marker post evaluation model and energy-saving prediction model through the device actual energy efficiency import model, and analyzing the energy efficiency change condition of a specified certain type of device or the whole life cycle of a certain device; and (3) analyzing the influence factors of the electric energy efficiency in terms of load rate, electric energy quality and the like under the condition of deviation of the rated working condition energy efficiency, and finding out the main factors of the electric energy efficiency and a method for improving the electric energy efficiency.
4. Regression training: and (3) performing algorithm matching by using expert balance scoring and a recurrent neural network, and matching the data mining target to a certain data mining algorithm. The patterns of interest, or the same representative set, are found in some representative table.
5. Outputting a result: the mined knowledge is used in the actual system or combined with knowledge of other systems or handed directly to the tissue of interest. And evaluating the found modes of the data mining by adopting a related method, and displaying the useful modes or the data describing the useful modes to a user in a form which is as visual as possible and readable and understandable by a human.
Therefore, the energy efficiency change condition of a certain type of equipment or the whole life cycle of the certain equipment is analyzed and specified through static energy efficiency evaluation and collection of the actual energy efficiency and the factory energy efficiency of the four model computing equipment; and (3) analyzing the influence factors of the electric energy efficiency in terms of load rate, electric energy quality and the like under the condition of deviation of the rated working condition energy efficiency, and finding out the main factors of the electric energy efficiency and a method for improving the electric energy efficiency.
Referring to fig. 4, the invention also discloses an electrical equipment energy efficiency evaluation device, which comprises:
the device screening module 11 is configured to obtain original device data of real-time device data, nameplate information and device installation information of each electrical device, and compare the original device data with standard nameplate information and standard device installation information to screen a target electrical device;
the energy efficiency evaluation module 12 is configured to input real-time equipment data of the target electrical equipment into a preset energy efficiency evaluation model, so that the preset energy efficiency evaluation model obtains an energy efficiency evaluation result of the target electrical equipment by analyzing and comparing the real-time equipment data with standard equipment energy efficiency data of a use stage corresponding to the target electrical equipment;
And a report generating module 13, configured to generate an energy saving report of the target electrical device based on the energy efficiency evaluation result.
As can be seen, the present application discloses obtaining original equipment data of real-time equipment data, nameplate information and equipment installation information of each electrical equipment, and comparing the original equipment data with standard nameplate information and standard equipment installation information to screen target electrical equipment; inputting the real-time equipment data of the target electrical equipment into a preset energy efficiency evaluation model so that the preset energy efficiency evaluation model can obtain an energy efficiency evaluation result of the target electrical equipment by analyzing and comparing the real-time equipment data with standard equipment energy efficiency data of a use stage corresponding to the target electrical equipment; and generating an energy saving report of the target electrical equipment based on the energy efficiency evaluation result. Therefore, through a static evaluation mode of the acquired original equipment data of each electric equipment, primary evaluation is carried out to screen out target electric equipment, then the real-time equipment data of the screened target electric equipment is input into a preset energy efficiency evaluation model, the real-time equipment data and the standard equipment energy efficiency data of the corresponding stage are analyzed and compared through the preset energy efficiency evaluation model, dynamic energy efficiency evaluation of each equipment index of the target electric equipment is realized, the energy efficiency evaluation result of the whole life cycle of the whole electric equipment is not a fixed evaluation result, multi-dimensional multi-angle energy efficiency evaluation is carried out on different use stages of the electric equipment, and after the energy efficiency evaluation result is acquired, energy saving advice is further carried out according to the energy efficiency evaluation result, so that a corresponding energy saving report is generated.
Further, the embodiment of the present application further discloses an electronic device, and fig. 5 is a block diagram of the electronic device 20 according to an exemplary embodiment, where the content of the figure is not to be considered as any limitation on the scope of use of the present application.
Fig. 5 is a schematic structural diagram of an electronic device 20 according to an embodiment of the present application. The electronic device 20 may specifically include: at least one processor 21, at least one memory 22, a power supply 23, a communication interface 24, an input output interface 25, and a communication bus 26. Wherein the memory 22 is used for storing a computer program, and the computer program is loaded and executed by the processor 21 to implement relevant steps in the electrical equipment energy efficiency evaluation method disclosed in any of the foregoing embodiments. In addition, the electronic device 20 in the present embodiment may be specifically an electronic computer.
In this embodiment, the power supply 23 is configured to provide an operating voltage for each hardware device on the electronic device 20; the communication interface 24 can create a data transmission channel between the electronic device 20 and an external device, and the communication protocol to be followed is any communication protocol applicable to the technical solution of the present application, which is not specifically limited herein; the input/output interface 25 is used for acquiring external input data or outputting external output data, and the specific interface type thereof may be selected according to the specific application requirement, which is not limited herein.
Processor 21 may include one or more processing cores, such as a 4-core processor, an 8-core processor, etc. The processor 21 may be implemented in at least one hardware form of DSP (Digital Signal Processing ), FPGA (Field-Programmable Gate Array, field programmable gate array), PLA (Programmable Logic Array ). The processor 21 may also comprise a main processor, which is a processor for processing data in an awake state, also called CPU (Central Processing Unit ); a coprocessor is a low-power processor for processing data in a standby state. In some embodiments, the processor 21 may integrate a GPU (Graphics Processing Unit, image processor) for rendering and drawing of content required to be displayed by the display screen. In some embodiments, the processor 21 may also include an AI (Artificial Intelligence ) processor for processing computing operations related to machine learning.
The memory 22 may be a carrier for storing resources, such as a read-only memory, a random access memory, a magnetic disk, or an optical disk, and the resources stored thereon may include an operating system 221, a computer program 222, and the like, and the storage may be temporary storage or permanent storage.
The operating system 221 is used for managing and controlling various hardware devices on the electronic device 20 and the computer program 222, so as to implement the operation and processing of the processor 21 on the mass data 223 in the memory 22, which may be Windows Server, netware, unix, linux, etc. The computer program 222 may further include a computer program that can be used to perform other specific tasks in addition to the computer program that can be used to perform the electrical device energy efficiency assessment method performed by the electronic device 20 as disclosed in any of the previous embodiments. The data 223 may include, in addition to data received by the electronic device and transmitted by the external device, data collected by the input/output interface 25 itself, and so on.
Further, the application also discloses a computer readable storage medium for storing a computer program; wherein the computer program, when executed by a processor, implements the electrical device energy efficiency assessment method disclosed previously. For specific steps of the method, reference may be made to the corresponding contents disclosed in the foregoing embodiments, and no further description is given here.
In this specification, each embodiment is described in a progressive manner, and each embodiment is mainly described in a different point from other embodiments, so that the same or similar parts between the embodiments are referred to each other. For the device disclosed in the embodiment, since it corresponds to the method disclosed in the embodiment, the description is relatively simple, and the relevant points refer to the description of the method section.
Those of skill would further appreciate that the various illustrative elements and algorithm steps described in connection with the embodiments disclosed herein may be implemented as electronic hardware, computer software, or combinations of both, and that the various illustrative elements and steps are described above generally in terms of functionality in order to clearly illustrate the interchangeability of hardware and software. Whether such functionality is implemented as hardware or software depends upon the particular application and design constraints imposed on the solution. Skilled artisans may implement the described functionality in varying ways for each particular application, but such implementation decisions should not be interpreted as causing a departure from the scope of the present application. The steps of a method or algorithm described in connection with the embodiments disclosed herein may be embodied directly in hardware, in a software module executed by a processor, or in a combination of the two. The software modules may be disposed in random access Memory RAM (Random Access Memory), memory, read-Only Memory ROM (Read Only Memory), electrically programmable EPROM (Electrically Programmable Read Only Memory), electrically erasable programmable EEPROM (Electric Erasable Programmable Read Only Memory), registers, hard disk, a removable disk, a CD-ROM (Compact Disc-Read Only Memory), or any other form of storage medium known in the art.
Finally, it is further noted that relational terms such as first and second, and the like are used solely to distinguish one entity or action from another entity or action without necessarily requiring or implying any actual such relationship or order between such entities or actions. Moreover, the terms "comprises," "comprising," or any other variation thereof, are intended to cover a non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements does not include only those elements but may include other elements not expressly listed or inherent to such process, method, article, or apparatus. Without further limitation, an element defined by the phrase "comprising one … …" does not exclude the presence of other like elements in a process, method, article, or apparatus that comprises the element.
The method, the device, the equipment and the medium for evaluating the energy efficiency of the electrical equipment provided by the invention are described in detail, and specific examples are applied to the principle and the implementation mode of the invention, and the description of the examples is only used for helping to understand the method and the core idea of the invention; meanwhile, as those skilled in the art will have variations in the specific embodiments and application scope in accordance with the ideas of the present invention, the present description should not be construed as limiting the present invention in view of the above.
Claims (10)
1. A method of evaluating energy efficiency of an electrical device, comprising:
acquiring original equipment data of real-time equipment data, nameplate information and equipment installation information of each piece of electrical equipment, and comparing the original equipment data with standard nameplate information and standard equipment installation information to screen target electrical equipment;
inputting the real-time equipment data of the target electrical equipment into a preset energy efficiency evaluation model so that the preset energy efficiency evaluation model can obtain an energy efficiency evaluation result of the target electrical equipment by analyzing and comparing the real-time equipment data with standard equipment energy efficiency data of a use stage corresponding to the target electrical equipment;
and generating an energy saving report of the target electrical equipment based on the energy efficiency evaluation result.
2. The electrical device energy efficiency evaluation method of claim 1, wherein comparing the raw device data with standard nameplate information and standard device installation information to screen for target electrical devices comprises:
and comparing the nameplate information and the equipment installation information of the electrical equipment in the original equipment data of each electrical equipment with standard nameplate information and standard equipment installation information in a preset equipment index library respectively so as to screen out non-scrapped electrical equipment as target electrical equipment.
3. The electrical device energy efficiency evaluation method according to claim 1, wherein before inputting the real-time device data of the target electrical device into a preset energy efficiency evaluation model, further comprising:
respectively establishing an energy efficiency index weight model for outputting an energy efficiency index weight proportion corresponding to real-time equipment data of each electric equipment, an equipment life cycle segmentation model for outputting an energy efficiency change condition corresponding to the life cycle of each electric equipment, a marker post evaluation model for comparing the energy consumption change condition of similar electric equipment and an energy saving energy prediction model for predicting corresponding energy efficiency after replacing the current electric equipment with the similar electric equipment;
and constructing a preset energy efficiency evaluation model based on the energy efficiency index weight model, the equipment life cycle segmentation model, the marker post evaluation model and the energy saving prediction model.
4. The electrical equipment energy efficiency evaluation method according to claim 3, wherein after the constructing a preset energy efficiency evaluation model based on the energy efficiency index weight model, the equipment life cycle segmentation model, the target evaluation model, and the energy saving prediction model, further comprising:
Performing model correction on the preset energy efficiency evaluation model by using expert knowledge and a preset time recurrent neural network to obtain a corrected energy efficiency evaluation model;
correspondingly, the inputting the real-time equipment data of the target electrical equipment into a preset energy efficiency evaluation model so that the preset energy efficiency evaluation model can analyze and compare the real-time equipment data with standard equipment energy efficiency data of a using stage corresponding to the target electrical equipment, and the method comprises the following steps:
and inputting the real-time equipment data of the target electrical equipment into the corrected energy efficiency evaluation model, so as to analyze and compare the real-time equipment data with the standard equipment energy efficiency data of the corresponding use stage of the target electrical equipment through the corrected energy efficiency evaluation model.
5. The electrical device energy efficiency evaluation method according to claim 4, wherein the analyzing and comparing the real-time device data with the standard device energy efficiency data of the use stage corresponding to the target electrical device by the modified energy efficiency evaluation model includes:
determining target energy efficiency data of the target electrical equipment based on the real-time equipment data through the corrected energy efficiency evaluation model;
And comparing and analyzing the target energy efficiency data with the standard equipment energy efficiency data of the corresponding use stage through the target pole evaluation model in the corrected energy efficiency evaluation model to obtain an energy efficiency evaluation result of the target electrical equipment.
6. The electrical device energy efficiency evaluation method of claim 5, wherein the determining target energy efficiency data of the target electrical device based on the real-time device data by the modified energy efficiency evaluation model comprises:
extracting the equipment characteristic quantity of the real-time equipment data through the energy efficiency index weight model in the corrected energy efficiency evaluation model, determining a target object from the equipment characteristic quantity, searching the nearest neighbors of the target object, and calculating and outputting the energy efficiency index weight proportion of the target electrical equipment based on the distance between the nearest neighbors; the device characteristic quantity is a numerical value or a numerical vector describing a device characteristic of the target electrical device;
and determining a current use stage of the target electrical equipment based on the real-time equipment data through the equipment life cycle segmentation model in the corrected energy efficiency evaluation model, and calculating target energy efficiency data of the target electrical equipment according to working condition information corresponding to the current use stage and the energy efficiency index weight proportion corresponding to the target electrical equipment.
7. The electrical device energy efficiency evaluation method according to any one of claims 1 to 6, characterized by further comprising:
counting the energy efficiency evaluation results of the similar electrical devices, and taking a target electrical device corresponding to the lowest device energy consumption in the energy efficiency evaluation results as a marker post electrical device;
and respectively storing nameplate information and equipment installation information of the standard post electrical equipment as standard nameplate information and standard equipment installation information in a preset equipment index library.
8. An electrical device energy efficiency evaluation apparatus, comprising:
the device screening module is used for acquiring original device data of real-time device data, nameplate information and device installation information of each electrical device, and comparing the original device data with standard nameplate information and standard device installation information to screen target electrical devices;
the energy efficiency evaluation module is used for inputting the real-time equipment data of the target electrical equipment into a preset energy efficiency evaluation model so that the preset energy efficiency evaluation model can acquire an energy efficiency evaluation result of the target electrical equipment by analyzing and comparing the real-time equipment data with standard equipment energy efficiency data of a corresponding use stage of the target electrical equipment;
And the report generation module is used for generating an energy-saving report of the target electrical equipment based on the energy efficiency evaluation result.
9. An electronic device, comprising:
a memory for storing a computer program;
processor for executing the computer program to implement the steps of the electrical device energy efficiency assessment method according to any one of claims 1 to 7.
10. A computer-readable storage medium storing a computer program; wherein the computer program when executed by a processor implements the steps of the electrical device energy efficiency assessment method according to any one of claims 1 to 7.
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