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Short-Term Load Forecasting Of Microgrid Via Hybrid Support Vector Regression And Long Short-Term Memory Algorithms, Arash Moradzadeh, Sahar Zakeri, Maryam Shoaran, Behnam Mohammadi-Ivatloo, Fazel Mohammadi
Short-Term Load Forecasting Of Microgrid Via Hybrid Support Vector Regression And Long Short-Term Memory Algorithms, Arash Moradzadeh, Sahar Zakeri, Maryam Shoaran, Behnam Mohammadi-Ivatloo, Fazel Mohammadi
Electrical and Computer Engineering Publications
© 2020 by the authors. Short-Term Load Forecasting (STLF) is the most appropriate type of forecasting for both electricity consumers and generators. In this paper, STLF in a Microgrid (MG) is performed via the hybrid applications of machine learning. The proposed model is a modified Support Vector Regression (SVR) and Long Short-Term Memory (LSTM) called SVR-LSTM. In order to forecast the load, the proposed method is applied to the data related to a rural MG in Africa. Factors influencing the MG load, such as various household types and commercial entities, are selected as input variables and load profiles as target …