A Comprehensive Evaluation and Prediction Method for Load Density Based on Big Data under Power Supply Partition Scenarios

In order to meet the requirements of power supply partition and grid planning, a comprehensive evaluation and mid-long term refined prediction method for load density based on big data under power supply scenarios is proposed, and similar units are clustered through the improved Agglomerative algorithm. The proposed method can effectively extract the typical features of various load densities, so as to reduce the requirement of the system for data sampling and provide support for the classified refined forecasting of various loads. Firstly, based on the data samples, the load density features of the plot samples in the grid are extracted with the kernel density estimation (KDE) method. Then, the entropy method is used to weight the eigenvalues to realize the evaluation of different types of load densities in each power supply unit, and further calculate the integrated load density level of the power supply units and power grids. Finally, the power supply units are clustered, and the parameters of the S-shaped growth curve are solved by the least square method, so as to realize the mid-long term prediction of various load densities. In case study, a detailed analysis is carried out, and the effectiveness of the method is verified by engineering examples.