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Impact Factor (2025): 6.9
DOI Prefix: 10.47001/IRJIET
Impact Factor (2025): 6.9
DOI Prefix: 10.47001/IRJIET
Vol 10 No 5 (2026): Volume 10, Issue 5, May 2026 | Pages: 726-732
International Research Journal of Innovations in Engineering and Technology
OPEN ACCESS | Research Article | Published Date: 31-05-2026
The deregulation of power sectors has transformed electricity into a highly volatile commodity. In competitive energy markets, accurate Short-Term Electricity Price Forecasting (STEPF) is crucial for market participants to optimize bidding strategies and minimize financial risks. However, electricity prices are highly non-linear, non-stationary, and exhibit multiple seasonalities. Traditional statistical models often fail to capture these complex patterns, especially during sudden price spikes. This paper proposes a novel hybrid deep learning architecture combining 1-Dimensional Convolutional Neural Networks (1DCNN) with Long Short-Term Memory (LSTM) networks. The 1D-CNN acts as a robust feature extractor for multivariable input data (historical prices, load demand, and weather variables), while the LSTM network captures the long-term temporal dependencies. Evaluated on real-world energy market data, the proposed CNN-LSTM model achieved a Mean Absolute Percentage Error (MAPE) of 6.1%, significantly outperforming traditional baseline models including ARIMA, Support Vector Regression (SVR), and standalone LSTM networks.
Electricity Price Forecasting, Deep Learning, CNN, LSTM, Deregulated Energy Market, Time-Series Analysis.
Manish Joshi, Preeti Rinhayat, Ajay Shyamkunwar, & Shailendra Turker. (2026). A Hybrid CNN-LSTM Deep Learning Framework for Day-Ahead Electricity Price Forecasting: A Comparative Study with Statistical and Machine Learning Models. International Research Journal of Innovations in Engineering and Technology - IRJIET, 10(5), 726-732.
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