Faculty, Staff and Student Publications
Language
English
Publication Date
7-2025
Journal
Machine Learning and Knowledge Extraction
DOI
doi.org/10.3390/make7040120
Abstract
Forecasting day-ahead electricity prices is a crucial research area. Both wholesale and retail sectors highly value improved forecast accuracy. Renewable energy sources have grown more influential and effective in the US power market. However, current forecasting models have shortcomings, including inadequate consideration of renewable energy impacts and insufficient feature selection. Many studies lack reproducibility, clear presentation of input features, and proper integration of renewable resources. This study addresses these gaps by incorporating a comprehensive set of input features, while these features are engineered to capture complex market dynamics. The model’s unique aspect is its inclusion of renewable-related inputs, such as temperature data for solar energy effects and wind speed for wind energy impacts on US electricity prices. The research also employs data preprocessing techniques like windowing, cleaning, normalization, and feature engineering to enhance input data quality and relevance. We developed four advanced hybrid deep learning models to improve electricity price prediction accuracy and reliability. Our approach combines variational mode decomposition (VMD) with four deep learning (DL) architectures: dense neural networks (DNNs), convolutional neural networks (CNNs), long short-term memory (LSTM) networks, and bidirectional LSTM (BiLSTM) networks. This integration aims to capture complex patterns and time-dependent relationships in electricity price data. Among these, the VMD-BiLSTM model consistently outperformed the others across all window implementations. Using 24 input features, this model achieved a remarkably low mean absolute error of 0.2733 when forecasting prices in the MISO market. Our research advances electricity price forecasting, particularly for the US energy market. These hybrid deep neural network models provide valuable tools and insights for market participants, energy traders, and policymakers.
Keywords
electricity price forecasting, renewable energy, a day-ahead market, USA energy market, neural network, deep learning, MISO, VMD, LSTM, CNN, Bi-LSTM, DNN
Published Open-Access
yes
Recommended Citation
Rahman Md. Saifur and Hassan Reza, "Hybrid Deep Learning Approaches for Accurate Electricity Price Forecasting: A Day-Ahead US Energy Market Analysis with Renewable Energy" (2025). Faculty, Staff and Student Publications. 1488.
https://digitalcommons.library.tmc.edu/uthsph_docs/1488