Electrical Load Forecasting Using LSTM, GRU, and RNN Algorithms

Mobarak Abumohsen, Amani Yousef Owda () and Majdi Owda
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Mobarak Abumohsen: Department of Natural, Engineering and Technology Sciences, Arab American University, Ramallah P600, Palestine
Amani Yousef Owda: Department of Natural, Engineering and Technology Sciences, Arab American University, Ramallah P600, Palestine
Majdi Owda: Faculty of Data Science, Arab American University, Ramallah P600, Palestine

Energies, 2023, vol. 16, issue 5, 1-31

Abstract: Forecasting the electrical load is essential in power system design and growth. It is critical from both a technical and a financial standpoint as it improves the power system performance, reliability, safety, and stability as well as lowers operating costs. The main aim of this paper is to make forecasting models to accurately estimate the electrical load based on the measurements of current electrical loads of the electricity company. The importance of having forecasting models is in predicting the future electrical loads, which will lead to reducing costs and resources, as well as better electric load distribution for electric companies. In this paper, deep learning algorithms are used to forecast the electrical loads; namely: (1) Long Short-Term Memory (LSTM), (2) Gated Recurrent Units (GRU), and (3) Recurrent Neural Networks (RNN). The models were tested, and the GRU model achieved the best performance in terms of accuracy and the lowest error. Results show that the GRU model achieved an R-squared of 90.228%, Mean Square Error (MSE) of 0.00215, and Mean Absolute Error (MAE) of 0.03266.

Keywords: load forecasting; machine learning; deep learning models; electric power system; short-term load forecasting (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (13)

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