基于人群搜索算法的电网短期用电负荷预测研究

Research on Short-Term Electric Load Forecasting of Power Grid Based on Crowd Search Algorithms

针对传统预测方法缺少对未知变量的分析,导致预测效果较差的问题,提出了基于人群搜索算法的电网短期用电负荷预测研究。将电力系统负荷时间序列进行分解,获取负荷时间序列的高低频分量,并对电网短期用电负荷特性展开分析。借助人工智能研究成果,研究人群搜索算法原理,充分考虑电网短期用电负荷受到温度因素影响,选取输入变量,并以此构建人群搜索算法预测模型。通过设计预测流程以及对未知变量分析,实现对电网短期用电负荷预测。通过实验结果可知,采用该预测方法具有良好预测效果,为保障电网稳定运行提供依据。

To address the problem that traditional forecasting method lacks analysis of unknown variables,which leads to poor forecasting effects,this paper proposes a short-term power load forecasting research based on crowd search algorithm.The load time series of the power system is decomposed to obtain the high and low frequency components of the load time series,and the short-term load characteristics of the power grid are analyzed.With the help of artificial intelligence research results,this paper studies the principle of crowd search algorithm,with full consideration given to the fact that the short-term power load of power grid is affected by temperature factors;and input variables are selected to build the prediction model of crowd search algorithm.The short-term load forecasting of power grid is realized by designing the forecasting process and analyzing unknown variables.The experimental results show that the method has good forecasting effect and provides a basis for ensuring the stable operation of the power grid.