Electrical Load Forecasting Using LSTM, GRU, and RNN Algorithms

• Published in: Energies (Basel) Vol. 16; no. 5; p. 2283
• Main Authors: Abumohsen, Mobarak, Owda, Amani Yousef, Owda, Majdi
• Format: Journal Article
• Language: English
• Published: Basel MDPI AG 01.03.2023
• Subjects: Accuracy; Algorithms; Customer services; Datasets; Deep learning; deep learning models; electric power system; Electricity; Energy consumption; Forecasting; Forecasting techniques; Infrastructure; Literature reviews; load forecasting; machine learning; Neural networks; short-term load forecasting; Palestinian Territories
• ISSN: 1996-1073; 1996-1073
• DOI: 10.3390/en16052283

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.