Information Entropy Based Neural Network Model for Short-Term Load Forecasting

With the development of electric market reform, short-term load forecasting (STLF) has been paid more and more attention. This paper presented a hybrid model to integrated information entropy and data mining theory with neural network to establish a new short-term load forecasting model. First, information entropy theory is used to select relevant ones from all influential factors; the results are used as inputs of neural network. Secondly, according to the features of power load, the typical historical load data samples were selected as the training set which have the same weather characteristic as the certain forecasting day by using data mining theory. Finally, Elman neural network forecasting model is constructed combining the reduced factors and typical training set. The presented model can effectively improve forecasting accuracy. The effectiveness of the model has been tested using Hebei province daily load data with satisfactory results