Prediction of Spanish Energy Pricing and Consumption Based on Hybrid Deep Learning Model

Sandeep Gupta; Vinita Rohilla; Sumeet Mathur; Swapnil Patil

doi:10.31893/multiscience.2025476

Keywords:

Spanish Energy Pricing

Abstract

Major obstacles that impact a stability and effectiveness of an energy system are faced by the energy supply industry, including the continuing COVID-19 epidemic and an ongoing crisis in Ukraine. This research, which focuses on Spain, emphasises the significance of electricity pricing as well as the need for precise models to calculate costs and usage. We used CNN, Bi-LSTM, and GRU, among others, to anticipate power usage and pricing using hourly data. To forecast energy prices and consumption in Spain, the authors suggest a model they term a hybrid CNN-Bi-LSTM-GRU model. To assess the suggested models' performance using the RMSE, MAE, and MAPE performance measures. For energy prices, it achieved a test RMSE of 5.032 and MAPE of 8.414, while for consumption, it attained a test RMSE of 18.92 and MAPE of 1.065, respectively.
References
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6. Kassem, Y., Gokcekus, H. and Aljatlawe, A. (2023) ‘Utilization of Solar Energy for Electric Vehicle Charging and the Energy Consumption of Residential Buildings in Northern Cyprus: A Case Study’, Engineering, Technology and Applied Science Research, 13(5), pp. 11598–11607. Available at: https://doi.org/10.48084/etasr.6142.
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8. Mirjalili, M.A. et al. (2023) ‘A comparative study of machine learning and deep learning methods for energy balance prediction in a hybrid building-renewable energy system’, Sustainable Energy Research [Preprint]. Available at: https://doi.org/10.1186/s40807-023-00078-9.
9. Müller, G. and Seibert, A. (2019) ‘Bayesian estimation of stable CARMA spot models for electricity prices’, Energy Economics [Preprint]. Available at: https://doi.org/10.1016/j.eneco.2018.10.016.
10. Patel, H. and Shah, M. (2021) ‘Energy Consumption and Price Forecasting Through Data-Driven Analysis Methods: A Review’, SN Computer Science [Preprint]. Available at: https://doi.org/10.1007/s42979-021-00698-2.
11. Ramos, P.V.B. et al. (2023) ‘Residential energy consumption forecasting using deep learning models’, Applied Energy [Preprint]. Available at: https://doi.org/10.1016/j.apenergy.2023.121705.
12. Rohilla, V., Chakraborty, D.S. and kumar, D.R. (2019) ‘Random Forest with Harmony Search Optimization for Location Based Advertising’, International Journal of Innovative Technology and Exploring Engineering [Preprint]. Available at: https://doi.org/10.35940/ijitee.i7761.078919.
13. Rohilla, V., Chakraborty, S. and Kaur, M. (2022) ‘Artificial Intelligence and Metaheuristic-Based Location-Based Advertising’, Scientific Programming [Preprint]. Available at: https://doi.org/10.1155/2022/7518823.
14. Rohilla, V., Kaur, M. and Chakraborty, S. (2022) ‘An Empirical Framework for Recommendation-based Location Services Using Deep Learning’, Engineering, Technology & Applied Science Research [Preprint]. Available at: https://doi.org/10.48084/etasr.5126.
15. Sahoo, S. et al. (2024) ‘Forecasting Tariff Rates and Enhancing Power Quality in Microgrids: The Synergistic Role of LSTM and UPQC’, Engineering, Technology and Applied Science Research, 14(1), pp. 12506–12511. Available at: https://doi.org/10.48084/etasr.6481.
16. Said, Y. and Alsariera, Y.A. (2024) ‘Hardware Implementation of a Deep Learning-based Model for Image Quality Assessment’, Engineering, Technology and Applied Science Research, 14(3), pp. 13815–13821. Available at: https://doi.org/10.48084/etasr.7194.
17. Sarswatula, S.A., Pugh, T. and Prabhu, V. (2022) ‘Modeling Energy Consumption Using Machine Learning’, Frontiers in Manufacturing Technology [Preprint]. Available at: https://doi.org/10.3389/fmtec.2022.855208.
18. Zhang, C. et al. (2020) ‘Deep learning for day-ahead electricity price forecasting’, IET Smart Grid [Preprint]. Available at: https://doi.org/10.1049/iet-stg.2019.0258.

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

How to cite

Gupta, S., Rohilla, V., Mathur, S., & Patil, S. (2025). Prediction of Spanish Energy Pricing and Consumption Based on Hybrid Deep Learning Model. Multidisciplinary Science Journal, (| Accepted Articles). https://doi.org/10.31893/multiscience.2025476

[1] Alkhudhayr, H. and Subahi, A. (2024) ‘Vehicle-to-Home : Implementation and Design of an Intelligent Home Energy Management System that uses Renewable Energy’, Engineering, Technology & Applied Science Research, 14(4), pp. 15239–15250.

[2] Bhat, V. (2023) ‘An Overview Of Essential Python Libraries And Tools For Data Science- A Review Paper’, International Journal of Creative Research Thoughts (IJCRT) www.ijcrt.org, 5(2), pp. 85–90. Available at: https://doi.org/10.33093/jetap.2023.5.2.10.

[3] Filipova-Petrakieva, S.K. and Dochev, V. (2022) ‘Short-Term Forecasting of Hourly Electricity Power Demand’, Engineering, Technology & Applied Science Research [Preprint]. Available at: https://doi.org/10.48084/etasr.4787.

[4] Gholizadeh, S. (2022) ‘Top Popular Python Libraries in Research’, Journal of Robotics and Automation Research, 3(2), pp. 3–7. Available at: https://doi.org/10.33140/jrar.03.02.02.

[5] Hernández Rodríguez, M. et al. (2023) ‘Artificial Intelligence-Based Prediction of Spanish Energy Pricing and Its Impact on Electric Consumption’, Machine Learning and Knowledge Extraction [Preprint]. Available at: https://doi.org/10.3390/make5020026.

[6] Kassem, Y., Gokcekus, H. and Aljatlawe, A. (2023) ‘Utilization of Solar Energy for Electric Vehicle Charging and the Energy Consumption of Residential Buildings in Northern Cyprus: A Case Study’, Engineering, Technology and Applied Science Research, 13(5), pp. 11598–11607. Available at: https://doi.org/10.48084/etasr.6142.

[7] Lu, H. et al. (2021) ‘Energy price prediction using data-driven models: A decade review’, Computer Science Review [Preprint]. Available at: https://doi.org/10.1016/j.cosrev.2020.100356.

[8] Mirjalili, M.A. et al. (2023) ‘A comparative study of machine learning and deep learning methods for energy balance prediction in a hybrid building-renewable energy system’, Sustainable Energy Research [Preprint]. Available at: https://doi.org/10.1186/s40807-023-00078-9.

[9] Müller, G. and Seibert, A. (2019) ‘Bayesian estimation of stable CARMA spot models for electricity prices’, Energy Economics [Preprint]. Available at: https://doi.org/10.1016/j.eneco.2018.10.016.

[10] Patel, H. and Shah, M. (2021) ‘Energy Consumption and Price Forecasting Through Data-Driven Analysis Methods: A Review’, SN Computer Science [Preprint]. Available at: https://doi.org/10.1007/s42979-021-00698-2.

[11] Ramos, P.V.B. et al. (2023) ‘Residential energy consumption forecasting using deep learning models’, Applied Energy [Preprint]. Available at: https://doi.org/10.1016/j.apenergy.2023.121705.

[12] Rohilla, V., Chakraborty, D.S. and kumar, D.R. (2019) ‘Random Forest with Harmony Search Optimization for Location Based Advertising’, International Journal of Innovative Technology and Exploring Engineering [Preprint]. Available at: https://doi.org/10.35940/ijitee.i7761.078919.

[13] Rohilla, V., Chakraborty, S. and Kaur, M. (2022) ‘Artificial Intelligence and Metaheuristic-Based Location-Based Advertising’, Scientific Programming [Preprint]. Available at: https://doi.org/10.1155/2022/7518823.

[14] Rohilla, V., Kaur, M. and Chakraborty, S. (2022) ‘An Empirical Framework for Recommendation-based Location Services Using Deep Learning’, Engineering, Technology & Applied Science Research [Preprint]. Available at: https://doi.org/10.48084/etasr.5126.

[15] Sahoo, S. et al. (2024) ‘Forecasting Tariff Rates and Enhancing Power Quality in Microgrids: The Synergistic Role of LSTM and UPQC’, Engineering, Technology and Applied Science Research, 14(1), pp. 12506–12511. Available at: https://doi.org/10.48084/etasr.6481.

[16] Said, Y. and Alsariera, Y.A. (2024) ‘Hardware Implementation of a Deep Learning-based Model for Image Quality Assessment’, Engineering, Technology and Applied Science Research, 14(3), pp. 13815–13821. Available at: https://doi.org/10.48084/etasr.7194.

[17] Sarswatula, S.A., Pugh, T. and Prabhu, V. (2022) ‘Modeling Energy Consumption Using Machine Learning’, Frontiers in Manufacturing Technology [Preprint]. Available at: https://doi.org/10.3389/fmtec.2022.855208.

[18] Zhang, C. et al. (2020) ‘Deep learning for day-ahead electricity price forecasting’, IET Smart Grid [Preprint]. Available at: https://doi.org/10.1049/iet-stg.2019.0258.

Abstract

References

How to cite