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A Novel Approach Based on Integration of Convolutional Neural Networks and Echo State Network for Daily Electricity Demand Prediction by Sujan Ghimire; Thong Nguyen-Huy; Mohanad S. AL-Musaylh; Ravinesh C. Deo; David Casillas-Pérez; Sancho Salcedo-Sanz is a Engineering article available to read on EtoBox.

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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

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Author

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

Publisher

Elsevier BV

Published

2023

Language

EN

Field

Engineering (Physical Sciences)