Half-hourly electricity price prediction with a hybrid convolution neural network-random vector functional link deep learning approach

Sujan Ghimire, Ravinesh C. Deo, David Casillas-Pérez, Ekta Sharma, Sancho Salcedo-Sanz, Prabal Datta Barua and U. Rajendra Acharya

Applied Energy, 2024, vol. 374, issue C, No 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 (GPI) values of approximately 1.61, 1.33, 1.85, 1.30, and 0.78 for Summer, Autumn, Winter, Spring, and Annual (Year 2022), respectively, outperforming alternative models. Similarly, the Kling–Gupta Efficiency (KGE) metrics for the proposed model consistently surpassed those of both decomposition-based and standalone models. For instance, the KGE value for MoDWT-CRVFL was approximately 0.972, significantly higher than values of approximately 0.958, 0.899, 0.963, 0.943, 0.930, 0.661, 0.708, 0.696, 0.739, and 0.738 for MoDWT-LSTM, MoDWT-DNN, MoDWT-XGB, MoDWT-RF, MoDWT-MLP, Bi-LSTM, LSTM, DNN, RF, XGB, and MLP, respectively. The methodologies proposed in this study optimize energy resource allocation, market prices, and network management, empowering market operators to make informed decisions for a resilient and efficient electricity market.

Keywords: Deep learning; Convolutional neural network; Maximum overlap discrete wavelet transform; Hybrid models; Random vector functional link (search for similar items in EconPapers)
Date: 2024
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DOI: 10.1016/j.apenergy.2024.123920

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