Power load forecasting algorithm based on wavelet packet analysis

This paper investigates the application of wavelet packet in power load forecasting, and it proposes a novel forecasting algorithm based on wavelet packet decomposition and reconstruction. The algorithm uses the biorthogonal wavelet that has linear phase to decompose the load data to extract load components of different frequencies, and then neural network is used to predict the load component of each wavelet packet space. Finally, the load forecasting values of all the spaces are added up to produce the load forecasting result. It is advantageous in analyzing the load characteristics in each time-frequency zone to achieve accurate modeling and forecasting. Case study shows that the proposed algorithm improves forecasting accuracy and it is superior to traditional back-propagation neural network.