Abstract
Ultra-Short-Term Load Forecasting plays an important role in Power Load Forecasting. Back Propagation Neural Network(BPNN) has become one of the most commonly used methods in Power System Ultra-Short-Term Load Forecasting for its ability of computing complex samples and training large-scale samples. However, traditional BPNN algorithm needs to set up a large amount of network training parameters, and it is easy to be trapped into local optima. A new algorithm which is Neural Network based on Dynamic Multi-Swarm Particle Swarm Optimizer (DMSPSO-NN) is proposed for Ultra-Short-Term Load Forecasting in this paper. DMSPSO-NN overcomes the shortage of traditional BPNN and has a good global search and higher accuracy which shows that it is suitable to be used for Ultra-Short-Term Load Forecasting.
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Liang, J.J., Song, H., Qu, B., Liu, W., Qin, A.K. (2014). Neural Network Based on Dynamic Multi-swarm Particle Swarm Optimizer for Ultra-Short-Term Load Forecasting. In: Tan, Y., Shi, Y., Coello, C.A.C. (eds) Advances in Swarm Intelligence. ICSI 2014. Lecture Notes in Computer Science, vol 8795. Springer, Cham. https://doi.org/10.1007/978-3-319-11897-0_44
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DOI: https://doi.org/10.1007/978-3-319-11897-0_44
Publisher Name: Springer, Cham
Print ISBN: 978-3-319-11896-3
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