摘要
电网负荷预测是电力系统高效运行的重要保障。针对传统预测方法在处理负荷波动性和复杂性方面的不足,文中设计了一种基于人工智能的电网负荷预测系统。首先,建立数据处理模块进行数据采集、清洗与特征工程,提升数据质量;其次,采用长短期记忆网络等人工智能算法构建负荷预测模型,实现高精度负荷预测;最后,设计预测结果应用模块,通过可视化与决策支持手段将预测信息应用于电网运行调度。实验结果表明,该系统有效提升了电网负荷预测精度,具有较强的实用价值。
Power grid load forecasting is an important guarantee for the efficient operation of the power system.In response to the shortcomings of traditional forecasting methods in dealing with load fluctuations and complexity,this paper designs an artificial intelligence based power grid load forecasting system.Firstly,establish a data processing module for data collection,cleaning,and feature engineering to improve data quality;Secondly,artificial intelligence algorithms such as long short-term memory networks are used to construct load forecasting models,achieving high-precision load forecasting;Finally,design a prediction result application module to apply the prediction information to power grid operation scheduling through visualization and decision support methods.The experimental results show that the system effectively improves the accuracy of power grid load forecasting and has strong practical value.
作者
于希彬
YU Xibin(Shandong Huake Information Technology Co.,Ltd.,Jinan 250000,China)
出处
《移动信息》
2025年第8期304-306,共3页
Mobile Information
关键词
电网负荷预测
人工智能
LSTM模型
Power grid load forecasting
Artificial intelligence
LSTM model
作者简介
于希彬(1990-),本科,研究方向为电力、电子通信。
