Wavelet Transform Support Vector Machine Based Load Forecasting Program

Short-term load forecasting is a program based on wavelet transform and support vector machine (SVM) for load prediction. The implementation involves performing wavelet transform decomposition on historical load data to extract multi-scale features, followed by applying SVM algorithms to build prediction models. This methodology combines the multi-resolution analysis capability of wavelet transforms with SVM's strong nonlinear fitting capacity, effectively capturing complex patterns in load data. Key implementation steps include: preprocessing raw load data, selecting appropriate wavelet bases (e.g., Daubechies wavelets) for decomposition, constructing feature vectors from wavelet coefficients, and training SVM models with kernel functions (like RBF kernel) for regression prediction. This hybrid approach significantly improves forecasting accuracy and reliability by handling nonlinear characteristics and temporal variations in power consumption patterns. Through this short-term load forecasting program, utilities can better anticipate future load trends, providing critical references for power system operation and dispatch decisions, thereby enhancing energy utilization efficiency and overall grid stability.