Short Term Load Forecasting Based on iForest-LSTM

被引：0
|
作者：
Ma, Yuan [1 ]
Zhang, Qian [1 ]
Ding, Jinjin [2 ]
Wang, Qiongjing [3 ]
Ma, Jinhui [4 ]
机构：
[1] Anhui Univ, Sch Elect Engn & Automat, Hefei, Peoples R China
[2] Anhui Elect Power Res Inst, Hefei, Anhui, Peoples R China
[3] Anhui Univ, Collaborat Innovat Ctr Ind Energy Saving & Power, Hefei, Anhui, Peoples R China
[4] State Grid Anhui Elect Power Co, Hefei, Anhui, Peoples R China
来源：
PROCEEDINGS OF THE 2019 14TH IEEE CONFERENCE ON INDUSTRIAL ELECTRONICS AND APPLICATIONS (ICIEA 2019) | 2019年
关键词：
Load forecasting; Isolation Forest; long short term memory; deep learning Introduction; REGRESSION;
D O I：
10.1109/iciea.2019.8833755
中图分类号：
T [工业技术];
学科分类号：
08 ;
摘要：
The noise generated by the measured data of the distribution network has impact on the accuracy of the load forecasting. In this paper, a short-term load forecasting method based on Isolation Forest (iForest) and Long Short-Term Memory (LSTM) neural network is proposed. Firstly, the iForest algorithm is used to mine and clean the abnormal historical load data. Secondly, a forecasting model is established based on the LSTM network in deep learning. Thirdly, the iForest-LSTM is formed, and then applied to the short-term load forecasting. Finally, the forecasting results of the iForest-LSTM method are compared with the standard LSTM and iForest-BP methods, and the proposed method can effectively improve the forecasting accuracy.
引用
收藏
页码：2278 / 2282
页数：5
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