现代电力2025，Vol.42Issue(2)：352-359,8.DOI:10.19725/j.cnki.1007-2322.2023.0036
基于自组织映射-前馈神经网络和先知混合模型的短期负荷预测
Short-term Load Forecasting Based on SOM-BP and Prophet Hybrid Model
摘要
Abstract
To improve the accuracy of short-term load fore-casting in power system,fully exploit the multi-dimensional in-formation in the historical data to better overcome the adverse effects caused by the lack of the historical data,a short-term load forecasting method based on the self-organizing maps and back propagation(abbr.SOM-BP)and Prophet hybrid model was proposed.Firstly,the similar day set was obtained by clus-tering the historical non-power data through SOM neural net-work,and then the BP neural network was trained with the sim-ilar day data to obtain the single point load value prediction res-ults.Secondly,focusing on the periodicity and temporal trends of historical data,the Prophet temporal model was used to per-form periodic nonlinear fitting on historical load data.Through historical data fitting error feedback,the key hyperparameters of the optimization model was adjust,and finally the short-term load forecasting results based on the combination of error recip-rocal method were obtained.Finally,Taking the power load data of a certain region as an example for verification,the res-ults show that the proposed improved prediction model has higher prediction accuracy and advantages in overcoming his-torical data deficiencies and fitting non-working day load curves.关键词
短期负荷预测/Prophet/自组织映射-前馈/神经网络/时间序列Key words
short-term load forecasting/Prophet/SOM-BP/neural network/time series分类
动力与电气工程引用本文复制引用
陈宇航,王渝红,南璐,何川,王腾鑫,张敏..基于自组织映射-前馈神经网络和先知混合模型的短期负荷预测[J].现代电力,2025,42(2):352-359,8.基金项目
国家电网公司总部科技项目(5100-202199274A-0-0-00).Project Supported by Science and Technology of SGCC(5100-202199274A-0-0-00). （5100-202199274A-0-0-00）
