| Summary: | The power system load is obviously affected by external factors, and the meteorological factor is one of the most important factors that change the load power. In order to improve the power grid’s adaptability to changing weather and to enhance its observable and measurable ability to load, this paper incorporates the random matrix theory, improves the traditional analytic hierarchy process, and proposes the fusion analytic hierarchy process (FAHP) as a new evaluation system construction method. A comprehensive evaluation study of meteorological factors and grid correlation indicators is carried out based on meteorological data and power grid active load data. Firstly, the augmented matrix method is leveraged to construct the data source matrix to determine the meteorological factor indicators and correlation analysis indicators. Secondly, the random matrix spectrum analysis theoretical Ring law and Pearson coefficient are established to complete the correlation analysis. Finally, FAHP is used to construct a comprehensive evaluation system of meteorological factor indicators. The proposed method can avoid the subjective factors of the traditional analytic hierarchy process, improving the accuracy and objectivity of weight distribution. It can effectively describe the correlation between active load and meteorological characteristics of the local area, providing strong support for subsequent index feature extraction and prediction of changes in load data under similar climatic characteristics.
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