An artificial intelligence-based user electricity actuarial system

Abstract

The present invention discloses a user power actuarial system based on artificial intelligence, which relates to the technical field of power intelligent services. The system includes the following components: Data acquisition and storage module: It is connected to the power grid and user power equipment through power sensors and communication interfaces, collects data such as user electricity bills and electricity consumption, as well as power grid electricity price information, and stores them in the database; The present invention deeply analyzes and models the data by using machine learning algorithms, predicts the user's future electricity consumption, load changes and electricity bill expenditures, analyzes the correlation between different electricity consumption behaviors and electricity bills, and generates targeted energy-saving suggestions for users. It not only considers the user's actual electricity consumption situation, but also combines information such as the real-time electricity price and peak-valley periods of the power grid, thus realizing the personalization and precision of energy-saving suggestions, helping users reasonably control electricity costs and optimize electricity consumption behaviors.

Description

Technical Field

[0001] The present invention relates to the technical field of intelligent power services, and specifically to a user power actuarial system based on artificial intelligence. Background Art

[0002] With the rapid development of society and the progress of technology, power services have become one of the indispensable infrastructures in modern society. Power demand forecasting and cost actuarial, as important components of power services, have an important impact on the operation and management of power enterprises and the electricity consumption experience of users. In recent years, the rise of artificial intelligence technology has provided a new opportunity for the intelligentization and refinement of power services. By integrating user power consumption behavior data and emotional data and using artificial intelligence algorithms for in-depth analysis, it is possible to achieve accurate prediction of user power demand and cost expenditure, thereby providing personalized and efficient power services for users.

[0003] Traditional power data analysis mainly relies on manual experience and basic statistical methods. These methods are unable to cope when dealing with modern power data with complexity and variability. Especially when faced with abnormal data caused by factors such as equipment failures, human violations, and data acquisition deviations, traditional methods are difficult to effectively detect and correct, resulting in inaccurate analysis results. In addition, traditional methods lack the ability to deeply explore the value of data and are difficult to provide accurate and personalized energy-saving strategies. Therefore, users often have difficulty obtaining practical and effective energy-saving suggestions in actual applications, and the effect of optimizing electricity costs is not obvious.

[0004] In view of the above problems, it is necessary to optimize the existing user power actuarial system based on artificial intelligence. By deeply analyzing and modeling the data, predicting the user's future electricity consumption, load changes, and electricity bill expenditure, and generating targeted energy-saving suggestions according to the actual electricity consumption situation. Therefore, it is of great significance to develop a user power actuarial system based on artificial intelligence that can comprehensively achieve the above characteristics. Summary of the Invention

[0005] The purpose of the present invention is to make up for the deficiencies of the existing technology and provide a user power actuarial system based on artificial intelligence. It can effectively detect and correct abnormal data by comprehensively collecting, processing, and analyzing power grid and user power-related data, combined with advanced time series analysis and artificial intelligence algorithms, improving the accuracy and reliability of data analysis. At the same time, the system can generate suitable energy-saving suggestions for users based on the analysis results, helping users optimize their electricity consumption behavior and reduce electricity costs. It not only improves the intelligent level of data analysis but also significantly enhances the pertinence and practicality of energy-saving suggestions, bringing new breakthroughs to the fields of power data analysis and energy-saving technology.

[0006] To solve the above technical problems, the present invention provides the following technical solutions: A user power accurate calculation system based on artificial intelligence, which system comprises the following components:

[0007] Data acquisition and storage module: Collect user electricity charges, electricity consumption and other data as well as grid electricity price information through the marketing data center, and store them in the database;

[0008] Data preprocessing and anomaly detection module: Preprocess the data in the database, use the multi-dimensional dynamic threshold method to monitor the power consumption data in real time, identify and analyze the anomaly points, and correct or mark the abnormal data;

[0009] Data analysis and modeling module: Analyze the data after preprocessing and anomaly correction using a comprehensive prediction model, establish models for predicting the user's future electricity consumption, load changes and electricity charge expenditures, and analyze the correlation between different electricity consumption behaviors and electricity charges;

[0010] Energy-saving suggestion generation module: Based on the results of data analysis and modeling, combined with the user's actual electricity consumption situation, comprehensively consider factors such as electricity consumption changes, electricity prices and implementation costs, use the energy-saving benefit evaluation formula to evaluate the energy-saving benefits and generate targeted energy-saving suggestions;

[0011] Interaction module: Deployed in the intranet of the power company to provide an interaction interface for the staff of the power supply company, display the energy-saving suggestion information corresponding to the user, and generate a corresponding user power accurate calculation report for the staff to download, and adjust the priority of the energy-saving suggestions according to the staff feedback and usage frequency factors to optimize the interaction experience and system effect.

[0012] Further, the data preprocessing and anomaly detection module uses the multi-dimensional dynamic threshold method to monitor the power consumption data in real time, identify and analyze the anomaly points, and its algorithm formula is: , where is the dynamic upper limit threshold of the th data dimension, is the dynamic lower limit threshold of the th data dimension, is the mean value of the th data dimension, is the standard deviation of the th data dimension, is the threshold coefficient of the th data dimension, adjusted according to the importance and fluctuation characteristics of the data in this dimension, is the time influence coefficient of the th data dimension, reflecting the influence of the data change trend over time on the threshold, is the time variable, representing the order or timestamp of the collected data, is the correlation influence coefficient of the th data dimension, and

[0013] or less than , then mark this data point as an abnormal point. For abnormal data whose accurate value can be determined, correct it according to the cause of the abnormality. For abnormal data caused by equipment failure, correct the abnormal data according to the normal operating parameters of the equipment. For abnormal data caused by data acquisition deviation, correct the abnormal data by re-acquiring data or calibrating the acquired data. For abnormal data whose accurate value cannot be determined, mark it as abnormal data, add a mark field to the data, and set the mark value of the abnormal data to a specific value.

[0014] Furthermore, the data preprocessing and anomaly detection module corrects or marks abnormal data. Specifically, traverse all data points of each data dimension, compare the value of the data point with the corresponding threshold. For the value of the data point greater than

[0015] Furthermore, the data analysis and modeling module uses machine learning technology to deeply analyze and model the data after preprocessing and anomaly correction. By learning and analyzing historical data, a prediction model is established to predict the user's future electricity consumption, load changes, and electricity bill expenditures. At the same time, analyze the correlation between different electricity consumption behaviors and electricity bills, find the key factors affecting electricity bills, continuously optimize and adjust the established model, evaluate the performance of the model using test data, and adjust the parameters of the model according to the evaluation results. , where

[0016] is the predicted electricity consumption, is the intercept term, which is calculated by performing a regression analysis on historical data, is the number of linear influencing factors, is the regression coefficient of the th linear influencing factor, is the th linear influencing factor, that is, the linear influencing factor data stored in the database, is the number of non-linear influencing factors, is the coefficient of the th non-linear influencing factor, is the function transformation of the th non-linear influencing factor, is the th non - linear influencing factor, is the error term, reflecting the random factors that the model cannot explain.

[0017] Furthermore, the energy - saving suggestion generation module uses an energy - saving benefit evaluation formula to evaluate the energy - saving benefit, and its formula is: , where, is the energy - saving benefit, that is, the amount of electricity bill saved after taking energy - saving measures, is the predicted electricity consumption after taking energy - saving measures, is the electricity consumption without taking energy - saving measures, is the average electricity price, is the evaluation time period, is the implementation cost coefficient of the energy - saving measures.

[0018] Furthermore, the energy - saving suggestion generation module generates targeted energy - saving suggestions. For the optimization of electricity - using time, according to the peak - valley periods of the electricity price, it is recommended that users use high - power electrical equipment during the valley period and avoid using electricity during the peak period; for the adjustment of the basic electricity charge collection method, when the basic electricity charge generated by the user is higher than that of other collection methods, a suggestion is put forward to the user to select the preferred basic electricity charge collection method according to their own production conditions; for the power factor adjustment, it is recommended that users take measures to improve the power factor and reduce the reactive power loss.

[0019] Furthermore, the interaction module adjusts the priority of the energy - saving suggestions according to the staff feedback and the usage frequency factor, and its priority evaluation formula is: , where, is the adjusted priority of the th energy - saving suggestion, is the priority of the th energy - saving suggestion before adjustment, is the staff feedback influence coefficient, used to control the influence degree of staff feedback on the suggestion priority, is the th staff feedback score of the energy - saving suggestion, and its value range is [-1, 1]. A positive number indicates that the staff is satisfied with the suggestion, and a negative number indicates dissatisfaction, is the usage frequency influence coefficient, used to control the influence degree of the suggestion usage frequency on the priority, is the th usage frequency of the energy - saving suggestion, calculated by the ratio of the number of times the user uses the suggestion to the total number of times the suggestions are used.

[0020] Compared with the prior art, this user - side electricity precise calculation system based on artificial intelligence has the following beneficial effects:

[0021] 1. The present invention deeply analyzes and models data by applying machine learning algorithms. By predicting the user's future electricity consumption, load changes, and electricity bill expenditures, and analyzing the correlation between different electricity consumption behaviors and electricity bills, personalized energy-saving suggestions are generated for users. It not only considers the user's actual electricity consumption situation but also combines information such as the real-time electricity price and peak-valley periods of the power grid, thus realizing the personalization and precision of energy-saving suggestions, helping users reasonably control electricity costs, optimize electricity consumption behaviors, and promoting the rational allocation and efficient utilization of power resources, which is of great significance for promoting energy conservation, emission reduction, and sustainable development.

[0022] 2. The present invention significantly improves the intelligent level of power data processing by introducing advanced artificial intelligence technologies. By real-time monitoring power consumption data, it effectively identifies and corrects abnormal data caused by factors such as equipment failures, human violations, or data collection deviations, thus ensuring the high accuracy and reliability of the data. This not only provides a solid foundation for subsequent data analysis and modeling but also greatly improves the efficiency and accuracy of power data management.

[0023] Other advantages, objectives, and features of the present invention will be described to some extent in the subsequent specification, and to some extent, will be obvious to those skilled in the art based on the study of the following text, or can be learned from the practice of the present invention. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] In order to more clearly illustrate the technical solutions in the embodiments of the present invention or in the prior art, the following will briefly introduce the drawings required for use in the description of the embodiments or the prior art. Obviously, the following drawings are only some embodiments of the present invention, and those of ordinary skill in the art can obtain other drawings without creative efforts based on these drawings.

[0025] Figure 1 It is a schematic structural diagram of a user power actuarial system based on artificial intelligence;

[0026] Figure 2 It is a flowchart of a user power actuarial system based on artificial intelligence. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0027] To further elaborate on the technical means and effects adopted by the present invention to achieve the predetermined invention objective, the following will, in conjunction with the accompanying drawings and preferred embodiments, detail the specific embodiments, structures, features, and their effects of the present invention as follows.

[0028] Embodiment 1

[0029] A large manufacturing electricity-consuming enterprise, mainly engaged in the production and manufacturing of auto parts, has a huge production workshop and numerous production equipment, such as stamping machines, CNC machine tools, welding equipment, etc. Its complex electricity consumption system makes the electricity cost account for a quite large proportion in the enterprise's cost. In order to effectively control costs and improve energy utilization efficiency, through the power supply company, this artificial intelligence-based user electricity actuarial system is applied to provide energy-saving suggestions for the enterprise's production.

[0030] The system collects data such as the enterprise's electricity cost, electricity consumption, load, power factor, etc. through the marketing data center, and at the same time obtains information such as the grid electricity price and peak-valley periods. These data are stored orderly in the system's powerful database, providing a data basis for subsequent analysis and processing.

[0031] Preprocess the collected data, use the multi-dimensional dynamic threshold method to monitor the power consumption data in real time, identify and analyze abnormal points, and conduct 24-hour real-time monitoring of the enterprise's power consumption data. Its algorithm formula is: , where is the dynamic upper threshold of the th data dimension, is the dynamic lower threshold of the th data dimension, is the mean value of the th data dimension, is the standard deviation of the th data dimension, is the threshold coefficient of the th data dimension, adjusted according to the importance and fluctuation characteristics of the data in this dimension, is the time influence coefficient of the th data dimension, reflecting the influence of the data's change trend over time on the threshold, is the time variable, representing the order or timestamp of the collected data, is the correlation influence coefficient of the th data dimension, is the comprehensive influence index of relevant data dimensions, obtained from the user electricity-related data stored in the database. During the monitoring process, it is found that the electricity consumption of a certain auto parts production line shows an abnormal increase during the period from 10:00 to 11:00 on Tuesday morning. The system quickly activates the abnormal cause analysis mechanism. By deeply analyzing the historical operation data of the production line equipment, including the changes in parameters such as the equipment's temperature, vibration, and current, and comparing with the electricity consumption behavior patterns of the same type of production lines, it is determined that a key motor of this production line has failed, resulting in a decrease in the motor's operating efficiency and a significant increase in energy consumption. The system clearly marks this abnormal data and immediately notifies the staff of the power supply company in the form of text messages and in-system notifications.

[0032] 8. Using machine learning algorithms to deeply analyze and model the pre - processed and anomaly - corrected data, its model formula is: , where

[0033] is the predicted electricity consumption, is the intercept term, which is calculated by performing a regression analysis on historical data, is the number of linear influencing factors, is the - th regression coefficient of the linear influencing factor, is the - th linear influencing factor, that is, the linear influencing factor data stored in the database, is the number of non - linear influencing factors, is the - th coefficient of the non - linear influencing factor, is the - th functional transformation of the non - linear influencing factor, is the - th non - linear influencing factor, is the error term, which reflects the random factors that the model cannot explain. By learning and analyzing the enterprise's electricity consumption data in the past year, the system can accurately predict the enterprise's future electricity consumption, load changes, and electricity bill expenditures. After detailed analysis, it is found that the enterprise has a large electricity consumption during peak hours, and some old equipment has low operating efficiency due to backward technology, which not only increases energy consumption but also leads to high electricity bill costs. For example, some stamping equipment purchased early has significantly higher energy consumption than new equipment and frequent breakdowns.

[0034] According to the results of data analysis and modeling, combined with the enterprise's actual production situation and development plan, the system generates a series of highly targeted and practical energy - saving suggestions for the enterprise, including adjusting the operating time of some non - critical equipment and arranging it to operate during valley hours to make full use of the advantages of low - valley electricity prices; suggesting that the enterprise choose an optimal basic electricity charge collection method according to its own production situation, etc. In addition, for power factor adjustment, it is recommended that the enterprise take measures to improve the power factor and reduce reactive power loss.

[0035] Power supply company staff can view energy-saving suggestions and detailed electricity cost analysis reports through the interactive interface, deeply understand the enterprise's electricity consumption situation and energy-saving potential. According to the actual production arrangements and cost budgets, the power supply company staff suggest that the enterprise make reasonable adjustments to the electricity usage plan and formulate a detailed energy-saving transformation plan. Three months after implementing the energy-saving suggestions, the enterprise feeds back the implementation effect through the system and finds that the electricity consumption has decreased by 12% compared with before, and the electricity cost has been reduced by about 15%. Based on the enterprise's feedback information, the system further optimizes the energy-saving suggestions and data analysis model, continuously monitors the enterprise's electricity usage, and provides more accurate and effective energy-saving strategies for the enterprise to achieve more efficient energy-saving effects and cost control.

[0036] Embodiment 2

[0037] An electricity-consuming enterprise in a commercial complex, located in the core business district of the city, includes various business forms such as large shopping malls, high-end office buildings, and diverse catering and entertainment venues. Its electricity consumption situation is complex and has obvious time-of-day and seasonal characteristics. To achieve the goals of energy conservation, emission reduction, and cost reduction in operation, the power supply company applies this artificial intelligence-based user power actuarial system to generate energy-saving suggestions for the enterprise.

[0038] The system establishes connections with various electricity-consuming devices in the commercial complex, such as large-area lighting systems, large central air-conditioning systems, multiple elevators, and catering and entertainment equipment, as well as the power grid system. According to the operation rules and electricity consumption characteristics of the commercial complex, the system collects data such as electricity costs, electricity quantities, loads, and power factors, and at the same time obtains information such as electricity prices and peak-valley periods of the power grid, and securely stores these data in the database for in-depth analysis later.

[0039] Strict preprocessing is performed on the stored data. Data cleaning technology is used to clean the data, removing noise and invalid information. The multi-dimensional dynamic threshold method is used to monitor the power consumption data and identify and analyze abnormal points. During the abnormal detection process, it is found that the lighting system on a certain floor has abnormal power consumption during non-business hours (from 2 am to 5 am), far higher than the normal level. The system immediately starts a detailed abnormal cause analysis process. By analyzing the operation records of relevant lighting equipment, checking the control switch status of the equipment, and comparing the electricity usage behavior patterns of other floors, it is judged that there may be electricity theft behavior or equipment leakage problems. The system marks this abnormal data as a key point and promptly notifies the enterprise's security and power management personnel by phone and email for a comprehensive investigation. After the joint inspection by security personnel and power technicians, it is finally determined that an aging and damaged lighting line on this floor has caused the leakage phenomenon.

[0040] Using machine learning algorithms to deeply analyze and model the processed data, combined with the historical electricity consumption data and real-time operation data of the commercial complex, the system can accurately predict the future electricity consumption, load changes, and electricity bill expenditures of the commercial complex. The analysis results show that the air-conditioning system in the shopping mall accounts for a relatively large proportion of electricity consumption in summer. Moreover, due to the aging of air-conditioning equipment, the refrigeration efficiency has decreased, resulting in high energy consumption. At the same time, the elevators in the office building have a high operation frequency during the peak commuting hours, and the empty running rate is also relatively high, causing unnecessary energy waste.

[0041] Based on the results of data analysis and modeling, combined with the operation characteristics and customer needs of the commercial complex, the system generates a series of comprehensive and targeted energy-saving suggestions for the enterprise. For example, adjust the operation time of some non-critical equipment and arrange it to operate during the valley period to make full use of the advantages of low valley electricity prices; put forward suggestions to the enterprise to choose the preferred basic electricity charge collection method according to its own production situation, etc. In addition, for power factor adjustment, it is recommended that the enterprise take measures to improve the power factor and reduce reactive power loss.

[0042] The staff of the power supply company learned about the energy-saving suggestions and electricity consumption data in detail through the interaction interface, and adjusted the operation strategy accordingly according to the actual operation situation and budget arrangement of the commercial complex. After half a year of implementing the energy-saving measures, the enterprise feedback the energy-saving effect to the system. The overall electricity consumption decreased by 10% compared with before, and the electricity cost was reduced by about 13%. At the same time, the indoor environmental comfort of the shopping mall and the office building was not affected. Instead, it was improved to a certain extent due to the application of the intelligent control system. The system optimizes the energy-saving suggestions and data analysis model according to the feedback information of the enterprise, continuously helps the enterprise tap the energy-saving potential, further reduces the electricity cost, improves the energy utilization efficiency, and at the same time enhances the overall operation level and competitiveness of the commercial complex.

[0043] The above is only a preferred embodiment of the present invention, and it does not impose any form of limitation on the present invention. Although the present invention has been disclosed as above with a preferred embodiment, it is not intended to limit the present invention. Any person skilled in the art can make some changes or modifications to the above-disclosed technical content to be equivalent embodiments within the scope of the technical solution of the present invention. However, as long as it does not depart from the content of the technical solution of the present invention, any brief modifications, equivalent changes, and modifications made to the above embodiments based on the technical essence of the present invention still fall within the scope of the technical solution of the present invention.

Claims

1. An artificial intelligence-based user electricity actuarial system, characterized in that The system includes the following components: Data acquisition and storage module: Collects users' electricity consumption, electricity quantity data, and grid electricity price information through the marketing data center and stores them in the database; Data preprocessing and anomaly detection module: Preprocesses the data in the database, uses the multi-dimensional dynamic threshold method to monitor the power consumption data in real time, identifies and analyzes the anomaly points, and corrects or marks the abnormal data; Data analysis and modeling module: Analyzes the data after preprocessing and anomaly correction using a comprehensive prediction model, establishes models for predicting users' future electricity consumption, load changes, and electricity bill expenditures, and analyzes the correlation between different electricity consumption behaviors and electricity bills; Energy-saving suggestion generation module: Based on the results of data analysis and modeling, combined with the actual electricity consumption of users, comprehensively considering factors such as changes in electricity consumption, electricity price, and implementation cost, it evaluates the energy-saving benefits using the energy-saving benefit evaluation formula and generates targeted energy-saving suggestions. The formula is: , where is the energy-saving benefit, that is, the amount of electricity bill saved after taking energy-saving measures, is the predicted electricity consumption after taking energy-saving measures, is the electricity consumption without taking energy-saving measures, is the average electricity price, is the evaluation time period, is the implementation cost coefficient of the energy-saving measures; Interaction Module: Deployed in the intranet of the power company to provide an interactive interface for the staff of the power supply company, display the energy-saving advice information of the corresponding users, generate the corresponding user power actuarial report for the staff to download, and adjust the priority of the energy-saving advice according to the staff feedback and usage frequency factors to optimize the interactive experience and system effect. Its priority evaluation formula is: , where is the adjusted priority of the th energy-saving advice, is the priority of the th energy-saving advice before adjustment, is the staff feedback impact coefficient, which is used to control the impact of staff feedback on the advice priority, is the th staff feedback score of the energy-saving advice, with a value range of [-1, 1]. A positive number indicates that the staff is satisfied with the advice, and a negative number indicates dissatisfaction, is the usage frequency impact coefficient, which is used to control the impact of the advice usage frequency on the priority, is the th usage frequency of the energy-saving advice, calculated by statistically the ratio of the number of times the user uses this advice to the total number of advice usage times.

2. The user power actuarial system based on artificial intelligence according to claim 1, wherein The data preprocessing and anomaly detection module uses a multi-dimensional dynamic threshold method to monitor power consumption data in real time, identify and analyze anomaly points. Its algorithm formula is: , where is the dynamic upper threshold of the th data dimension, is the dynamic lower threshold of the th data dimension, is the mean of the th data dimension, is the standard deviation of the th data dimension, is the threshold coefficient of the th data dimension, which is adjusted according to the importance and fluctuation characteristics of the th data dimension, is the time influence coefficient of the th data dimension, reflecting the influence of the data change trend over time on the threshold, is the time variable, representing the order or timestamp of the collected data, is the correlation influence coefficient of the th data dimension, is the comprehensive influence index of relevant data dimensions, which is obtained from the user's power-related data stored in the database.

3. The user power actuarial system based on artificial intelligence according to claim 2, characterized in that, The data preprocessing and anomaly detection module corrects or marks the anomaly data. Specifically, it traverses all data points in each data dimension, compares the value of the data point with the corresponding threshold. For the value of the data point greater than or less than , the data point is marked as an anomaly point. For the anomaly data whose accurate value can be determined, it is corrected according to the cause of the anomaly. For the anomaly data caused by equipment failure, the anomaly data is corrected according to the normal operating parameters of the equipment. For the anomaly data caused by data acquisition deviation, the anomaly data is corrected by re-acquiring data or calibrating the acquired data. For the anomaly data whose accurate value cannot be determined, it is marked as anomaly data, a marker field is added to the data, and the marker value of the anomaly data is set to a specific value .

4. An artificial intelligence-based user electricity actuarial system according to claim 1, characterized in that, The data analysis and modeling module uses machine learning technology to deeply analyze and model the data after preprocessing and anomaly correction. By learning and analyzing historical data, a prediction model is established to predict users' future electricity consumption, load changes, and electricity bill expenditures. At the same time, the correlation between different electricity consumption behaviors and electricity bills is analyzed to find the key factors affecting electricity bills, continuously optimize and adjust the established model, evaluate the performance of the model using test data, and adjust the model parameters according to the evaluation results.

5. An artificial intelligence-based user electricity actuarial system according to claim 4, characterized in that, The data analysis and modeling module establishes a prediction model through learning and analyzing historical data, and its model formula is: , where is the predicted electricity consumption, is the intercept term, which is calculated by performing a regression analysis on historical data, is the number of linear influencing factors, is the regression coefficient of the is the th linear influencing factor, that is, the linear influencing factor data stored in the database, is the number of non - linear influencing factors, is the coefficient of the is the functional transformation of the is the th non - linear influencing factor, is the error term, which reflects the random factors that the model cannot explain.

6. An artificial intelligence-based user electricity actuarial system according to claim 1, characterized in that The energy-saving suggestion generation module generates targeted energy-saving suggestions. For optimizing the electricity usage time, according to the peak and valley periods of the electricity price, it is recommended that users use high-power electrical equipment during the valley period and avoid using electricity during the peak period; for adjusting the basic electricity charge collection method, when the basic electricity charge generated by the user is higher than that of other collection methods, a suggestion is put forward to the user to choose the optimal basic electricity charge collection method according to their own production situation, that is, a targeted strategy based on the data analysis and modeling results and combined with the user's actual electricity consumption situation; for power factor adjustment, it is recommended that users take measures to improve the power factor and reduce reactive power loss.

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