Abstract
To overcome the defects of neural network (NN) using back-propagation algorithm (BPNN) such as slow convergence rate and easy to fall into local minimum, the particle swarm optimization (PSO) algorithm was adopted to optimize BPNN model for short-term load forecasting (SLTF). Since those defects are partly caused by the random selection of network’s initial values, PSO was used to optimize initial weights and thresholds of BPNN model, thus a novel model for STLF was built, namely PSO-BPNN model. The simulation results of daily and weekly loads forecasting for actual power system show that the proposed forecasting model can effectively improve the accuracy of SLTF and this model is stable and adaptable for both workday and rest-day. Furthermore, its forecasting performance is far better than that of simple BPNN model and BPNN model using genetic algorithm to determine the initial values (GA-BPNN).
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Niu, DX., Zhang, B., Xing, M. (2006). Application of Neural Network Based on Particle Swarm Optimization in Short-Term Load Forecasting. In: Wang, J., Yi, Z., Zurada, J.M., Lu, BL., Yin, H. (eds) Advances in Neural Networks - ISNN 2006. ISNN 2006. Lecture Notes in Computer Science, vol 3972. Springer, Berlin, Heidelberg. https://doi.org/10.1007/11760023_184
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DOI: https://doi.org/10.1007/11760023_184
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