Half-hourly electricity price prediction with a hybrid convolution neural network-random vector functional link deep learning approach
Article
Ghimire, Sujan, Deo, Ravinesh C., Casillas-Perez, David, Sharma, Ekta, Salcedo-sanz, Sancho, Barua, Prabal and Acharya, U. Rajendra. 2024. "Half-hourly electricity price prediction with a hybrid convolution neural network-random vector functional link deep learning approach." Applied Energy. 374. https://doi.org/10.1016/j.apenergy.2024.123920
Article Title | Half-hourly electricity price prediction with a hybrid convolution neural network-random vector functional link deep learning approach |
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ERA Journal ID | 4005 |
Article Category | Article |
Authors | Ghimire, Sujan, Deo, Ravinesh C., Casillas-Perez, David, Sharma, Ekta, Salcedo-sanz, Sancho, Barua, Prabal and Acharya, U. Rajendra |
Journal Title | Applied Energy |
Journal Citation | 374 |
Article Number | 123920 |
Number of Pages | 34 |
Year | 2024 |
Publisher | Elsevier |
Place of Publication | United Kingdom |
ISSN | 0306-2619 |
1872-9118 | |
Digital Object Identifier (DOI) | https://doi.org/10.1016/j.apenergy.2024.123920 |
Web Address (URL) | https://www.sciencedirect.com/science/article/pii/S0306261924013035 |
Abstract | Digital technologies with predictive modelling capabilities are revolutionizing electricity markets, especially in demand-side management. Accurate electricity price prediction is essential in deregulated markets; however, developing effective models is challenging due to high-frequency fluctuations and price volatility. This study introduces a hybrid prediction system that addresses these challenges through a comprehensive data processing and modelling framework for half-hourly electricity price predictions. The preprocessing stage employs the Maximum Overlap Discrete Wavelet Transform (MoDWT) to enhance input quality by reducing overlap and revealing underlying price patterns. The prediction model integrates Convolutional Neural Networks with Random Vector Functional Link (CRVFL) in a deep learning hybrid approach. Bayesian Optimization fine-tunes the MoDWT-CRVFL model for optimal performance. Validation of the model is conducted using half-hourly electricity prices from New South Wales. The results highlight the efficacy of the MoDWT-CRVFL model, achieving high accuracy with superior Global Performance Indicator ( |
Keywords | Convolutional neural network; Deep learning; Maximum overlap discrete wavelet transform; Hybrid models; Random vector functional link |
Contains Sensitive Content | Does not contain sensitive content |
ANZSRC Field of Research 2020 | 461103. Deep learning |
Byline Affiliations | School of Mathematics, Physics and Computing |
Rey Juan Carlos University, Spain | |
University of Alcala, Spain | |
School of Business | |
Cogninet Australia, Australia | |
University of Technology Sydney |
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