Short-term load forecasting with weather component based on improved extreme learning machine

Abstract

For improving the accuracy and speed of short-term power load forecasting, a new on-line power load-forecasting method based on regularized fixed-memory extreme learning machine (RFM-ELM) is proposed. This method can choose the prediction model adaptively and adjust model parameters automatically. Considering uncertain factors, actual load data and real time meteorological data are used to train a forecasting model based on RFM-ELM, which improves the load forecasting accuracy effectively. RFM-ELM adopts the latest training sample and abandons the oldest training sample iteratively to achieve the online training of network weights. The structural risk is integrated into the model in order to enhance the generalization ability and robustness of the load-forecasting model. This paper verifies the method and the model by using the real data of a region. Experimental results show that the method significantly increases the precision of prediction. This approach provides superior accuracy and adaptability compared with the method of ELM when applied in short-term load forecasting.