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Open AccessArticle

Deep Learning-Assisted Short-Term Load Forecasting for Sustainable Management of Energy in Microgrid

1
Faculty of Electrical and Computer Engineering, University of Tabriz, Tabriz 5166/15731, Iran
2
Department of Electrical and Computer Engineering, University of Porto, 4000-008 Porto, Portugal
*
Author to whom correspondence should be addressed.
Inventions 2021, 6(1), 15; https://doi.org/10.3390/inventions6010015
Received: 3 January 2021 / Revised: 25 January 2021 / Accepted: 29 January 2021 / Published: 3 February 2021
(This article belongs to the Special Issue Emerging Technologies for the Energy Systems of the Future)
Nowadays, supplying demand load and maintaining sustainable energy are important issues that have created many challenges in power systems. In these types of problems, short-term load forecasting has been proposed as one of the management and energy supply modes in power systems. In this paper, after reviewing various load forecasting techniques, a deep learning method called bidirectional long short-term memory (Bi-LSTM) is presented for short-term load forecasting in a microgrid. By collecting relevant features available in the input data at the training stage, it is shown that the proposed procedure enjoys important properties, such as its great ability to process time series data. A microgrid in rural Sub-Saharan Africa, including household and commercial loads, was selected as the case study. The parameters affecting the formation of household and commercial load profiles are considered as input variables, and the total household and commercial load profiles of the microgrid are considered as the target. The Bi-LSTM network is trained by input variables to forecast the microgrid load on an hourly basis by recognizing the consumption pattern. Various performance evaluation indicators such as the correlation coefficient (R), mean squared error (MSE), and root mean squared error (RMSE) are utilized to analyze the forecast results. In addition, in a comparative approach, the performance of the proposed method is compared and evaluated with other methods used in similar studies. The results presented for the training phase show an accuracy of R = 99.81% for the Bi-LSTM network. The test and load forecasting stage are performed by the Bi-STLM network, with an accuracy of R = 99.34% and forecasting errors of MSE = 0.1042 and RMSE = 0.3243. The results confirm the high performance of the proposed Bi-LSTM technique, with a high correlation coefficient when compared to other methods used for short-term load forecasting. View Full-Text
Keywords: energy management; microgrid; residential and commercial loads; short-term load forecasting; deep learning; bidirectional long short-term memory (Bi-LSTM) energy management; microgrid; residential and commercial loads; short-term load forecasting; deep learning; bidirectional long short-term memory (Bi-LSTM)
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MDPI and ACS Style

Moradzadeh, A.; Moayyed, H.; Zakeri, S.; Mohammadi-Ivatloo, B.; Aguiar, A.P. Deep Learning-Assisted Short-Term Load Forecasting for Sustainable Management of Energy in Microgrid. Inventions 2021, 6, 15. https://doi.org/10.3390/inventions6010015

AMA Style

Moradzadeh A, Moayyed H, Zakeri S, Mohammadi-Ivatloo B, Aguiar AP. Deep Learning-Assisted Short-Term Load Forecasting for Sustainable Management of Energy in Microgrid. Inventions. 2021; 6(1):15. https://doi.org/10.3390/inventions6010015

Chicago/Turabian Style

Moradzadeh, Arash; Moayyed, Hamed; Zakeri, Sahar; Mohammadi-Ivatloo, Behnam; Aguiar, A. P. 2021. "Deep Learning-Assisted Short-Term Load Forecasting for Sustainable Management of Energy in Microgrid" Inventions 6, no. 1: 15. https://doi.org/10.3390/inventions6010015

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