基于神经网络的电力短期负荷预测方法研究
金建峰，高健
（国网浙江省电力有限公司湖州供电公司，浙江 湖州 313000）
摘要： 电能作为清洁、高效、安全的二次能源，是推动中国能源转型升级的关键领域。电力负荷预测是电力系统规
划的重要组成部分，可以帮助维持电能生产与消耗之间的平衡，对稳步推进分布式可再生能源的大规模并网至关重要。
在此基础上，系统分析了基于神经网络的电力短期负荷预测的4 种方法及其相关应用实践，提出要进一步完善神经网络
算法的适用性和提高模型的准确率和稳定性，并采用多算法融合的神经网络进行电力短期负荷预测，这对电力短期负荷
预测实践具有一定的参考和借鉴意义。
关键词： 能源转型；神经网络；电力负荷；预测方法
中图分类号： TM715；TP183      文献标志码： A      文章编号： 2095-0802-(2023)07-0041-04
Short-term Power Load Forecasting Method Based on Neural Network
JIN Jianfeng, GAO Jian
(Huzhou Power Supply Company, State Grid Zhejiang Electric Power Co., Ltd., Huzhou 313000, Zhejiang, China)
Abstract: As a clean, efficient and safe secondary energy source, electric energy is a key area in promoting China's energy
transformation and upgrading. Power load forecasting is an important component of power system planning, which can help maintain
a balance between electricity production and consumption, and is crucial for steadily promoting the large-scale grid connection of
distributed renewable energy. On this basis, four methods and the related application practices in short -term power load
forecasting based on neural network were systematically analyzed. It was proposed to further improve the applicability of neural
network algorithms and improve the accuracy and stability of the model, and to use a multi-algorithm fusion neural network for
short-term power load forecasting, which has certain reference significance for the practice of short-term power load forecasting.

Key words: energy transformation; neural network; power load; forecasting method