Research On The Correlation And Comprehensive Evaluation Of Meteorological And Power Consumption Laws Based On Random Matrix

Today,China has made significant progress in the construction of smart grids,further expanding the network scale and diversifying its components.This poses many challenges for analyzing network stability and accurately predicting load,while providing enormous development potential.With the advancement of smart grid construction,traditional methods of power system modeling and analysis gradually expose their shortcomings,while data mining technology continues to inject new vitality into the development of smart grid.Therefore,mastering the key technologies of power data is very important for the digital empowerment of the power industry and the construction of new power systems.Load behavior analysis is the basis for stable operation and load forecasting of power grids.Load behavior directly affects the accuracy of load forecasting,and is very important for the safe and reliable operation of power grids.However,charge behavior is also affected by many factors,including weather factors.There is no known reference formula,and it is usually random,difficult to follow,complex coupling,and volatility.Today’s complex and volatile weather environment adds more challenges to mastering the changing laws of load characteristics.Therefore,how to quickly and accurately use historical and real-time data in the power grid to extract and quantify the correlation between meteorological factors and load consumption behavior,and build a comprehensive evaluation system for the correlation indicators between meteorological factors and active load data,screening effective weather factors is very important for improving the accuracy of load forecasting and assisting in subsequent power planning decisions.The current research on the correlation between weather factors and grid charging behavior has the following shortcomings:1)Existing correlation studies focus on the impact of a single weather factor(such as temperature or humidity)on charge changes.However,the weather factors that affect the perception of environmental comfort are not just temperature or humidity.Weather factors such as wind speed,direction,intensity and duration of sunlight,precipitation,and atmospheric pressure can all affect users’ electricity consumption behavior.Therefore,the study of a single weather factor is unilateral and does not constitute a comprehensive analytical framework.2)In the existing research on the correlation between coupled meteorological factors and payload data,the analysis of coupled meteorological indicators is relatively independent,and there is no multi-angle comparative analysis of coupled meteorological factors.For example,analyzing the cumulative and delayed effects of weather factors on the electrical behavior of electric charges leads to the dispersion of different weather factors in actual use.Effective indicators cannot be filtered for specific areas,are inefficient in use,take a long time to calculate,and do not allow rapid and effective use of network information under complex weather conditions.3)The existing evaluation system mainly focuses on the construction of low-carbon networks,market peak correction,and distribution network fault risk assessment.Weather factors are one of the important factors that affect network conditions.There is a lack of comprehensive and systematic research on the impact of this factor on network operation,and there is a lack of horizontal proportional relationship between indicators.The innovation points of this article are summarized as follows:This paper applies random matrix theory to the study of the correlation between meteorological factors and payloads,making full use of the advantages of random matrix theory in processing high-quality data,such as fast data processing speed,high data load,and large matrix composition,effectively reflecting the cumulative effects of meteorological factors.Potential correlation between weather factors and ACT charges.