电力负荷短期预测研究  

Research on Short-term Power Load Forecasting

作　　者：罗文[1] Luo Wen

机构地区：[1]贵州省交通规划勘察设计研究院股份有限公司,贵州贵阳550000

出　　处：《电力系统装备》2022年第1期57-59,共3页Electric Power System Equipment

摘　　要：为进行电力负荷的短期预测,本文建立一种以支持向量机为核心算法的短期数据预测模型,同时通过小波分解算法对研究数据进行处理,从而进一步提高模型的预测精度。同时,针对以往预测模型预测适应性较低的问题,本文提出了建立模型的可调节参数,通过改变可调节参数使得模型可以完成更多供电区域的预测,具有良好的适应性。实验结果显示,本模型在对电力负荷进行短期预测的过程中,表现出了良好的精确度和适应性。In order to carry out the short-term prediction of power load,this paper establishes a short-term data prediction model with support vector machine as the core algorithm,and processes the research data through wavelet decomposition algorithm,so as to further improve the prediction accuracy of the model.At the same time,in view of the low prediction adaptability of the previous prediction models,this paper proposes the adjustable parameters of the model.By changing the adjustable parameters,the model can complete the prediction of more power supply areas and has good adaptability.The experimental results show that the model shows good accuracy and adaptability in the process of short-term power load forecasting.

关 键 词：负荷预测 小波分解 支持向量机 可调参数 

分 类 号：TM715[电气工程—电力系统及自动化]