Key Technologies of Agent Power Purchasing Service Evaluation Based on Multidimensional Correlation and Fine-Grainedness

This study constructs a framework for evaluating proxy power purchasing services based on multidimensional associations, aiming to improve the performance and value of proxy power purchasing services by comprehensively analyzing the complex relationships among multidimensional attributes such as electricity price, electricity consumption, and supply and demand balance. The study first proposes a multifaceted feature space model, using covariance matrix and radial basis kernel function (RBF) to capture linear and nonlinear correlations, providing a theoretical basis for data-driven service optimization. Subsequently, the study designs a dataset with a hierarchical time series structure. It combines the entropy method to select key attributes to ensure the scientificity and comprehensiveness of the evaluation indicators. Fine-grained evaluation indicators include response time, cost optimization rate, and fluctuation coefficient, which accurately measure service quality. Principal component analysis (PCA) dimensionality reduction and fuzzy clustering techniques are used for dimensional optimization to improve the evaluation efficiency. Experimental results show that the new model is significantly superior to traditional models and competitors regarding response time, cost savings, price fluctuation control, personalized demand satisfaction, seasonal adaptability, and emergency response capabilities. The study verifies the effectiveness of multidimensional association analysis and provides practical guidance for the scientific management of the power market.