A new method for short-term load forecasting integrating fuzzy-rough sets with artificial neural network

Abstract

Short term load forecasting (STLF) has an essential role in the operation of electric power systems. In recent years, artificial neural networks (ANN) are more commonly used for load forecasting. However, there still exist some difficulties in choosing the input variables and selecting an appropriate architecture of the networks. This paper presents a novel fuzzy-rough sets based ANN for STLF. The fuzzy-rough sets theory is first employed to perform input selection and determine the initial weights of ANN. In the sequel, an improved k-nearest neighbor (K-NN) method is used for the selection of similar days in history as the training set of ANN. Then ANN module is trained using historical daily load and weather data selected to perform the final forecast. To demonstrate the effectiveness of the approach, short-term load forecasting was performed on the Hang Zhou Electric Power Company in China, and the testing results show that the proposed model is feasible and promising for load forecasting