摘要
随着智能电网建设的深入推进,电网负荷预测精度对保障系统安全稳定运行具有重要意义。针对传统负荷预测方法在面对复杂场景时预测精度不足的问题,提出一种基于大数据分析的电网负荷预测优化算法。该算法融合长短期记忆网络(LSTM)模型、时间序列模型(FbProphet)及深度学习等方法构建多层预测模型。实验结果表明,所提算法在国网莱芜供电公司实际运行环境下取得显著效果,预测准确率达98%,有效识别负荷高峰,累计削减尖峰负荷超过870 MW,为电网安全稳定运行提供有力支撑。
With the deepening of the construction of smart grids,the accuracy of grid load prediction is of great significance to ensuring the safe and stable operation of the system.Aiming at the problem of insufficient prediction accuracy of traditional load forecasting methods when facing complex scenarios,a power grid load forecasting optimization algorithm based on big data analysis is proposed.The algorithm combines long-term and short-term memory networks,time series model FbProphet,and deep learning methods to build a multi-layered prediction model.Experimental results show that the proposed algorithm achieves remarkable results in the actual operating environment of State Grid Laiwu Power Supply Company.The prediction accuracy rate reaches 98%,effectively identifies peak load,and reduces peak load by more than 870 MW cumulatively,providing strong support for the safe and stable operation of the power grid.
出处
《科技创新与应用》
2025年第4期98-101,共4页
Technology Innovation and Application
关键词
电网负荷预测
大数据分析
LSTM
深度学习
优化算法
power grid load forecasting
big data analysis
LSTM
deep learning
optimization algorithm
