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 and Fazel Mohammadi
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Arash Moradzadeh: Faculty of Electrical and Computer Engineering, University of Tabriz, 5166616471 Tabriz, Iran
Sahar Zakeri: Faculty of Electrical and Computer Engineering, University of Tabriz, 5166616471 Tabriz, Iran
Maryam Shoaran: Faculty of Electrical and Computer Engineering, University of Tabriz, 5166616471 Tabriz, Iran
Behnam Mohammadi-Ivatloo: Faculty of Electrical and Computer Engineering, University of Tabriz, 5166616471 Tabriz, Iran
Fazel Mohammadi: Department of Electrical and Computer Engineering, University of Windsor, Windsor, ON N9B 1K3, Canada

Sustainability, 2020, vol. 12, issue 17, 1-17

Abstract: 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 variables. Identifying the behavioral patterns of input variables as well as modeling their behavior in short-term periods of time are the major capabilities of the hybrid SVR-LSTM model. To present the efficiency of the suggested method, the conventional SVR and LSTM models are also applied to the used data. The results of the load forecasts by each network are evaluated using various statistical performance metrics. The obtained results show that the SVR-LSTM model with the highest correlation coefficient, i.e., 0.9901, is able to provide better results than SVR and LSTM, which have the values of 0.9770 and 0.9809, respectively. Finally, the results are compared with the results of other studies in this field, which continued to emphasize the superiority of the SVR-LSTM model.

Keywords: Energy Management; Long Short-Term Memory (LSTM); Machine Learning; Microgrid (MG); Short-Term Load Forecasting (STLF); Support Vector Regression (SVR) (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (9)

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