Electricity Load Forecasting Based on Adaptive Quantum-Behaved Particle Swarm Optimization and Support Vector Machines on Global Level

With the development of electronic industry, accurate load forecasting of the future electricity demand plays an important role in regional or national power system strategy management. Electricity load forecasting is complex to conduct due to its nonlinearity of influence factors. Support vector machine (SVM) is a novel type of learning machine, which has been successfully employed to solve nonlinear regression and time series problems. In this paper, A short-term load forecasting model based on SVM with adaptive quantum-behaved particle swarm optimization algorithm (AQPSO) is presented. By introducing a diversity-guided model into the quantum-behaved particle swarm optimization (QPSO), the AQPSO algorithm is proposed and then employed to determine the free parameters of SVM model automatically. The model is proved to be able to enhance the accuracy and improve global convergence ability and reduce operation time by numerical experiments. Subsequently, examples of electricity load data from a city in China are used to illustrate the performance of the proposed model.The empirical results reveal that the proposed model outperforms the other models.Therefore,the approach is efficient and practical to short-term load forecasting of electric power system.