A novel approach based on integration of convolutional neural networks and echo state network for daily electricity demand prediction

Sujan Ghimire, Thong Nguyen-Huy, Mohanad S. AL-Musaylh, Ravinesh C. Deo, David Casillas-Pérez and Sancho Salcedo-Sanz

Energy, 2023, vol. 275, issue C

Abstract: Predicting electricity demand data is considered an essential task in decisions taking, and establishing new infrastructure in the power generation network. To deliver a high-quality electricity demand prediction, this paper proposes a hybrid combination technique, based on a deep learning model of Convolutional Neural Networks and Echo State Networks, named as CESN. Daily electricity demand data from four sites (Roderick, Rocklea, Hemmant and Carpendale), located in Southeast Queensland, Australia, have been used to develop the proposed hybrid prediction model. The study also analyzes five other machine learning-based models (support vector regression, multilayer perceptron, extreme gradient boosting, deep neural network, and Light Gradient Boosting) to compare and evaluate the outcomes of the proposed deep learning approach. The results obtained in the experimental study showed that the proposed hybrid deep learning model is able to obtain the highest performance compared to other existing models developed for daily electricity demand data forecasting. Based on the statistical approaches utilized in this study, the proposed hybrid approach presents the highest prediction accuracy among the compared models. The obtained results showed that the proposed hybrid deep learning algorithm is an excellent and accurate electricity demand forecasting method, which outperformed the state of the art algorithms that are currently used in this problem.

Keywords: Electricity demand forecasting; Sustainable energy; Artificial intelligence; Deep learning; Echo state networks; Convolutional neural networks; Hybrid algorithms (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (12)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:energy:v:275:y:2023:i:c:s0360544223008241

DOI: 10.1016/j.energy.2023.127430

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