Research on Power Load Forecasting Method Based on Multi-Factor Fusion Characteristics

Short-term power load forecasting is of great significance for improving the operational efficiency of the power grid, optimizing energy dispatching and ensuring the safety and stability of the power system. This paper comprehensively considers key influencing variables such as climatic factors and date types, and constructs a hybrid prediction model combining Discrete Wavelet Transform (DWT) and Support Vector Machine (SVM). Firstly, the load data is decomposed into multiple scales through DWT to extract key features and suppress noise interference. Subsequently, the improved t-SNE and Principal component Analysis (PCA) were adopted to reduce the dimension and fuse the high-dimensional features in order to enhance the data representation ability. Finally, high-precision short-term load forecasting is achieved based on SVM. Experiments show that this model significantly improves the prediction accuracy by mining the correlation characteristics between historical load patterns and weather, providing reliable technical support for dynamic power grid planning, efficient consumption of renewable energy, and power market decision-making.