Short-Term Load Forecasting of Microgrid via Hybrid Support Vector Regression and Long Short-Term Memory Algorithms

Arash Moradzadeh,Fazel Mohammadi,Behnam Mohammadi-Ivatloo,Maryam Shoaran,Sahar Zakeri

doi:10.3390/su12177076

Arash Moradzadeh, Fazel Mohammadi + Show 3 more

Open Access

https://doi.org/10.3390/su12177076

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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.

Highlights

Increasing the number of electrical energy consumers has caused problems, such as reduced reliability and stability in traditional power systems
In this paper, the Support Vector Regression (SVR)-Long Short-Term Memory (LSTM) is applied to the dataset related to the MG load in Sub-Saharan Africa
To present the efficiency of the designed method, the conventional SVR and LSTM models are applied to the considered data

Summary

Introduction

Increasing the number of electrical energy consumers has caused problems, such as reduced reliability and stability in traditional power systems. To face such problems and better demand response, power systems must increase their generation capacity. There are other problems, such as increased fossil fuel consumption and environmental pollutions [1,2]. As the energy crisis and the environmental crisis become more serious, Distributed Generations (DGs), as the main forms of Renewable Energy Sources (RESs), have attracted much attention in issues related to energy management and sustainability of the power systems. The concept of the smart grid is mainly comprised of a Microgrid (MG) as the main component [4,5]

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