Abstract
Increasingly complex economic backgrounds and power consumption have created more difficulties for medium and long-term load forecasting. Traditional load forecasting models such as linear regression and time series models have a good effect on linear data, but the accuracy of nonlinear electric load data in Xinjiang is poor. The BP neural network model has better processing ability for nonlinear data, but it also has the problem of poor fitting and generalization ability. Aiming at this problem, this paper establishes a medium- and long-term power load forecasting model of Bayesian regularized BP neural network. Compared with the general gradient descent method BP neural network model, it is proved that the proposed model has a good effect on the electricity consumption forecast in Xinjiang.
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Yuan, C., Niu, D., Li, C., Sun, L., Xu, L. (2020). Electricity Consumption Prediction Model Based on Bayesian Regularized BP Neural Network. In: Xu, Z., Choo, KK., Dehghantanha, A., Parizi, R., Hammoudeh, M. (eds) Cyber Security Intelligence and Analytics. CSIA 2019. Advances in Intelligent Systems and Computing, vol 928. Springer, Cham. https://doi.org/10.1007/978-3-030-15235-2_76
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DOI: https://doi.org/10.1007/978-3-030-15235-2_76
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