An Electric Load Forecasting Model Based on BP Neural Network and Improved Bat Algorithm Hybridized with Extremal Optimization

Electric load forecasting is a vital role in obtaining effective management of modern power systems. The accuracy forecasting results will lead to the improvement of the energy efficiency and reduction of production cost. This paper presents a novel electric load forecasting model by using BP neural network and improved bat algorithm with extremal optimization called IBA-EO-BP model. First, to enhance the global search ability and diversity of original bat algorithm (BA), we propose IBA-EO by improving original BA and combining with extremal optimization. Then, considering traditional BP is more likely converge to local optimal values, the IBA-EO is employed to find out the optimal connection weight parameters in BP. Two datasets from energy market operation in Australia are selected as case study. The simulation results demonstrate that the proposed IBA-EO-BP model is much more accurate than the traditional BP forecasting model and persistence model in terms of three widely used performance indices and two statistical tests.