朱耿,王波,贺旭,虞殷树,白文博
ZHU Geng,WANG Bo,HE Xu,YU Yinshu,BAI Wenbo

• 1:国网浙江省电力有限公司宁波供电公司
• 2:宁波市电力设计院有限公司

摘要(Abstract)：

短期电力负荷的准确预测是电力系统安全经济运行的重要条件。为了提高电力系统短期负荷预测的准确性，提出一种基于关键特征优化的电力系统短期负荷预测方法。首先，对影响电力系统短期负荷的气象特征、日类型特征和历史负荷特征的构建方法进行优化，为负荷预测模型提供更多先验知识；然后，考虑输入特征和输出预测向量的特点，构建结合卷积神经网络与全连接层的短期电力负荷预测模型；最后，通过算例验证基于关键特征优化的电力系统短期负荷预测方法在实际负荷预测任务中的效果。算例结果表明，对气象特征、日类型特征和历史负荷特征等关键特征的优化，均有利于提升电力系统短期负荷预测的准确性。
Accurate forecasting of short-term power load is an important condition for the safe and economic operation of the power system. To improve the accuracy of short-term load forecasting for the power system, a short-term load forecasting method based on key feature optimization is proposed. Firstly, the construction method of the meteorological features, daily type features and historical load features affecting the short-term load of the power system is optimized, which can provide more prior knowledge for the load forecasting model. Then, considering the characteristics of the input features and the output prediction vector, a short-term power load forecasting model combining the convolutional neural network and the fully connected layer is constructed. Finally, the effect of the short-term load forecasting method for the power system based on the key feature optimization in the actual load forecasting task is validated by a numerical example. The example result shows that the key feature optimization of meteorological features, daily type features and historical load features is conducive to improving the accuracy of the short-term load forecasting for the power system.

关键词(KeyWords)： 特征优化;负荷预测;卷积神经网络;全连接层
feature optimization;load forecasting;convolutional neural network;fully connected layer

Abstract:

Keywords:

基金项目(Foundation): 宁波永耀电力投资集团有限公司科技项目（CY820400QT20210652）

作者(Author): 朱耿,王波,贺旭,虞殷树,白文博
ZHU Geng,WANG Bo,HE Xu,YU Yinshu,BAI Wenbo

DOI: 10.19585/j.zjdl.202308006

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