185 / 2021-10-30 19:31:16
Short-term Load Prediction Based on The Combination of K-means and Random Forest
K-means algorithm; Low load rate; Load forecasting; Power supply and distribution scheme; Random forest;
全文被拒
Weiwei Chai / State Grid Gansu Electric Power Company
Lei Qin / Lanzhou Jiaotong University
Haiying Dong / Lanzhou Jiaotong University
Chunshan Sun / State Grid Gansu Electric Power Company
Wei Shen / State Grid Gansu Electric Power Company
Shiyun Qiao / State Grid Gansu Electric Power Company
Aiming at the problem that the power supply and distribution system runs at low load rate for a long time and wastes capacity due to the expansion of the power supply and distribution system, a short-term load forecasting method combining K-means and random forest is proposed. The proposed method divides power users into four categories based on electricity behavior, based on which the corresponding category load data is selected as the input sample of the random forest model to obtain short-term load prediction results. Example analysis shows that this method can ensure the rapid clustering accuracy, and effectively realize the short-term prediction of power load based on the random forest, to achieve the purpose of improving the load rate.
重要日期
  • 会议日期

    07月11日

    2023

    08月18日

    2023

  • 11月10日 2021

    初稿截稿日期

  • 12月10日 2021

    注册截止日期

  • 12月11日 2021

    报告提交截止日期

主办单位
IEEE IAS
承办单位
IEEE IAS Student Chapter of Southwest Jiaotong University (SWJTU)
IEEE IAS Student Chapter of Huazhong University of Science and Technology (HUST)
IEEE PELS (Power Electronics Society) Student Chapter of HUST
移动端
在手机上打开
小程序
打开微信小程序
客服
扫码或点此咨询