基于时间序列和改进的反向传播神经网络的区域电能替代电位预测

IF 4.6 Q1 OPTICS Journal of Physics-Photonics Pub Date : 2023-11-01 DOI:10.1088/1742-6596/2636/1/012029
Ziwen Cai, Yutao Xu, Yong Xiao, Dunhui Chen, Yun Zhao, Zhukui Tan
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引用次数: 0

摘要

摘要提出了一种基于时间序列的回归预测与GRA-IPSO-BP结构相结合的预测方法,利用基于三指数平滑预测方法的时间序列模型预测电力替代预测。利用GRA-IPSO-BP结构对预测结果进行了校正。算法结果表明,与单一方法预测相比,将时间序列与GRA-IPSO-BP结构相结合可以显著提高电力替代的预测精度。
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Regional electrical energy substitution potential prediction based on time series and improved back propagation neural network
Abstract A combined time-series-based regression forecasting and GRA-IPSO-BP structure is proposed to forecast the electricity replacement forecasts using a time series model based on the triple exponential smoothing forecasting method. The forecast results are corrected using the GRA-IPSO-BP structure. The results of the algorithm show that using a combination of time series and GRA-IPSO-BP structure can significantly improve the forecasting accuracy of electricity replacement compared to single-method forecasting.
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来源期刊
CiteScore
10.70
自引率
0.00%
发文量
27
审稿时长
12 weeks
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