基于ARIMA的风电发电量预测  

Power Generation Forecasting of Wind-generated Electricity Based on ARIMA

作　　者：刘吉雄 邹瑞 许思为 LIU Jixiong;ZOU Rui;XU Siwei(Wuhan Electric Apparatus Research Institute Co.,Ltd.,Wuhan 430050,China;Hubei University,Wuhan 430062,China)

机构地区：[1]武汉电器科学研究所有限公司,湖北武汉430050 [2]湖北大学,湖北武汉430062

出　　处：《现代信息科技》2025年第4期157-161,166,共6页Modern Information Technology

摘　　要：风能作为一种可再生清洁能源,得到了高速发展。高精度的风力发电量预测可为电力调度以及平抑并网波动提供重要依据。文章基于ARIMA模型,分别进行超短期、短期和中长期风力发电量预测,探讨其可行性和有效性。使用某风电场2019年1月1日至2020年12月31日的风电功率数据进行建模,并开展不同时间长度的预测。在此基础上,考虑数据的周期性特征以及实际气候因素对发电功率的影响,优化模型的预测效果。实验结果表明,考虑数据周期性与外生变量的影响能够显著提升对实际功率的预测精度。超短期、短期和中长期预测的均方根误差分别为7.16、12.63和17.98。Wind energy,as a renewable and clean energy source,is developing at a high speed.High-precision wind power generation forecasting can provide an important basis for power dispatching and mitigating grid-connection fluctuations.Based on the ARIMA model,this paper conducts ultra-short-term,short-term,and medium-to-long-term wind power generation forecasts to explore its feasibility and effectiveness.The wind power data of a wind farm from January 1,2019 to December 31,2020 is used for modeling and forecasting over different time lengths.On this basis,considering the periodic characteristics of the data and the influence of actual climate factors on power generation,the forecasting effect of the model is optimized.The experimental results show that considering the influence of periodic data and exogenous variables can significantly improve the forecasting accuracy of actual power.The RMSE of ultra-short-term,short-term,and medium-to-long-term forecasts are 7.16,12.63 and 17.98,respectively.

关 键 词：风力发电 发电量预测 ARIMA 皮尔逊系数 

分 类 号：TP39[自动化与计算机技术—计算机应用技术] TM614[自动化与计算机技术—计算机科学与技术]