Short-Term Load Forecasting Using MATLAB's Wavelet Toolbox and Neural Network Toolbox

Using MATLAB's Wavelet Toolbox and Neural Network Toolbox, we can implement short-term load forecasting systems. This technique analyzes historical load data to predict future electricity demand patterns. The Wavelet Toolbox enables multi-resolution analysis through functions like wavedec() and waverec(), which decompose signals into different frequency components using discrete wavelet transforms. Meanwhile, the Neural Network Toolbox provides functions such as feedforwardnet() and train() to create and train neural networks that learn complex load patterns and trends through backpropagation algorithms. By integrating these toolboxes - typically through wavelet decomposition of load signals followed by neural network training on the decomposed components - we achieve more accurate and reliable load forecasting results. This integrated approach helps optimize energy supply planning and demand management strategies, with implementation typically involving data preprocessing, wavelet coefficient extraction, neural network architecture design, and prediction validation cycles.