Electrical Load Forecasting Using LSTM, GRU, and RNN Algorithms

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 for...

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Bibliographic Details
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
Online Access:Get full text
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Summary: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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ISSN:1996-1073
1996-1073
DOI:10.3390/en16052283