Multi-region electricity demand prediction with ensemble deep neural networks

• Published in: PloS one Vol. 18; no. 5; p. e0285456
• Main Authors: Irfan, Muhammad, Shaf, Ahmad, Ali, Tariq, Zafar, Mariam, Rahman, Saifur, Mursal, Salim Nasar Faraj, AlThobiani, Faisal, A Almas, Majid, Attar, H M, Abdussamiee, Nagi
• Format: Journal Article
• Language: English
• Published: United States Public Library of Science 18.05.2023; Public Library of Science (PLoS)
• Subjects: Accuracy; Algorithms; Analysis; Artificial neural networks; Bias; Biology and Life Sciences; Business metrics; Computer and Information Sciences; Deep learning; Electric power demand; Electric utilities; Electricity; Electricity consumption; Energy consumption; Energy expenditure; Energy management; Energy management systems; Environmental sustainability; Forecasting; Humidity; Load; Long short-term memory; Machine learning; Management; Methods; Modelling; Neural networks; Physical Sciences; Power consumption; Predictions; Rain; Recurrent neural networks; Renewable resources; Research and Analysis Methods; Root-mean-square errors; Short term memory; Support vector machines; Time series; Saudi Arabia; United States; China
• ISSN: 1932-6203; 1932-6203
• DOI: 10.1371/journal.pone.0285456

Electricity consumption prediction plays a vital role in intelligent energy management systems, and it is essential for electricity power supply companies to have accurate short and long-term energy predictions. In this study, a deep-ensembled neural network was used to anticipate hourly power utilization, providing a clear and effective approach for predicting power consumption. The dataset comprises of 13 files, each representing a different region, and ranges from 2004 to 2018, with two columns for the date, time, year and energy expenditure. The data was normalized using minmax scalar, and a deep ensembled (long short-term memory and recurrent neural network) model was used for energy consumption prediction. This proposed model effectively trains long-term dependencies in sequence order and has been assessed using several statistical metrics, including root mean squared error (RMSE), relative root mean squared error (rRMSE), mean absolute bias error (MABE), coefficient of determination (R2), mean bias error (MBE), and mean absolute percentage error (MAPE). Results show that the proposed model performs exceptionally well compared to existing models, indicating its effectiveness in accurately predicting energy consumption.