Abstract
Medium and long-term power load forecasting is the basis for power system planning and construction. This paper builds a prediction model based on SaDE-SVM algorithm. In order to reduce its selection problem of excessive large-scale hyperplane parameters, improve global optimization ability of traditional SVM, and further improve the prediction accuracy of SVM, the SaDE-SVM optimization algorithm is proposed. This algorithm optimizes the training process of traditional SVM based on adaptive differential evolution algorithm. The results of the medium and long-term forecasting for China’s power load show that the improved SaDE-SVM algorithm has good adaptability, robustness, fast convergence rate, and high accuracy for multi-influencing factors prediction model with less data volume, and is applicable to relevant medium and long-term forecasts.
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Huang, Y., Zhang, L., Shi, M., Liu, S., Xu, S. (2018). Medium and Long-Term Forecasting Method of China’s Power Load Based on SaDE-SVM Algorithm. In: Qiao, J., et al. Bio-inspired Computing: Theories and Applications. BIC-TA 2018. Communications in Computer and Information Science, vol 951. Springer, Singapore. https://doi.org/10.1007/978-981-13-2826-8_42
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DOI: https://doi.org/10.1007/978-981-13-2826-8_42
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