| 史 静,李冰洁,李泽森,胡晓燕,李 琥.基于改进LSTM的电力系统多层级负荷预测方法研究[J].电力需求侧管理,2025,27(2):62-67 |
| 基于改进LSTM的电力系统多层级负荷预测方法研究 |
| Research on multi-level load forecasting method for power system based on improved LSTM |
| 投稿时间：2024-11-26 修订日期：2025-01-02 |
| DOI：10. 3969 / j. issn. 1009-1831. 2025. 02. 010 |
| 中文关键词: 多层级负荷预测 LSTM算法 负荷要素 气象因素 社会经济因素 |
| 英文关键词: multilevel load forecasting LSTM algorithm load factors meteorological factors socioeconomic factors |
| 基金项目:国网江苏省电力有限公司经济技术研究院项目（SGJSJY00GHJS2400074） |
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| 中文摘要: |
| 电力负荷预测是电力系统制定发展规划和发电计划的基础,电网负荷数据量庞大、结构复杂、统计口径多样,影响负荷变化的因素多变,新能源的大规模接入进一步增大了电力负荷预测的预测难度。提出一种基于改进的长短时记忆网络的电力系统多层级负荷预测方法,建立省级-市级-变电站级的多层级负荷的时间序列关系,将不同负荷层级的历史负荷数据、气象数据和区域经济数据作为预测算法的输入,并将各层级负荷进行要素分类,从规划发展的角度加入约束条件,采用基于改进的LSTM的3层堆叠神经网络预测模型完成各层级负荷的整体预测。仿真算例以华东S省Y市的实际电力负荷数据及光伏出力数据为基础进行计算,结果表明所提方法对于提高多层级电力负荷的预测精度具有较优效果。 |
| 英文摘要: |
| Power load forecasting is the basis of power system development planning and power generation plan. The load data of power grid is huge, complicated in structure and diverse in statistical scope, and the factors affecting load change are changeable. The large-scale access of new energy further increases the difficulty of power load forecasting. A multi-level load forecasting method of power system based on improved long short term memory（LSTM）is proposed, which establishes the time series relationship of multi-level load at the provincial,municipal and substation levels, takes historical load data, meteorological data and regional economic data of different load levels as input of the forecasting algorithm, and classifies the load factors at each level. Constraints are added from the perspective of planning development, and the three-layer stacked neural networks prediction model based on improved LSTM is used to complete the overall prediction of each level of load. The simulation example is based on the actual power load data and PV output data of S province and Y city in East China. The results show that the proposed method has a good effect on improving the prediction accuracy of multi-level power load. |
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