摘要
研究了基于人工智能技术的智能电网负荷预测与调度优化方法,针对负荷波动和可再生能源不确定性,构建了长短期记忆网络(LSTM)预测模型,并结合鲁棒优化、随机优化和智能优化策略,全面提升电网运行效率和可靠性。仿真实验验证了所提方法在预测精度、经济性和环保性方面的显著优势,为智能电网建设提供了理论依据与技术支持。
In this paper,the study based on artificial intelligence technology of intelligent grid load forecasting and scheduling optimization method,in view of the load fluctuation and renewable energy uncertainty,build the long and short-term memory network(LSTM)prediction model,combined with robust optimization,random optimization and intelligent optimization strategy,improve power grid operation efficiency and reliability.The simulation experiment verifies the significant advantages of the proposed method in terms of prediction accuracy,economy and environmental protection,and provides theoretical basis and technical support for the construction of smart grid.
作者
张云
ZHANG Yun(State Grid Kunshan Power Supply Company,Kunshan 215300,China)
出处
《电工技术》
2025年第S1期270-272,275,共4页
Electric Engineering
关键词
智能电网
负荷预测
调度优化
人工智能
smart grid
load forecasting
scheduling optimization
artificial intelligence
