Open Access
Open access
Energies, volume 16, issue 5, pages 2283

Electrical Load Forecasting Using LSTM, GRU, and RNN Algorithms

Mobarak Abumohsen 1
Amani Yousef Owda 1
Majdi Owda 2
1
 
Department of Natural, Engineering and Technology Sciences, Arab American University, Ramallah P600, Palestine
2
 
Faculty of Data Science, Arab American University, Ramallah P600, Palestine
Publication typeJournal Article
Publication date2023-02-27
Journal: Energies
scimago Q1
wos Q3
SJR0.651
CiteScore6.2
Impact factor3
ISSN19961073
Electrical and Electronic Engineering
Energy Engineering and Power Technology
Renewable Energy, Sustainability and the Environment
Building and Construction
Control and Optimization
Engineering (miscellaneous)
Energy (miscellaneous)
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.

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