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Predictive Modeling of Renewable Energy Purchase Prices Using Deep Learning Based on Polish Power Grid Data for Small Hybrid PV Microinstallations

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  • Michał Pikus

    (Department of Applied Computer Science, Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, AGH University of Krakow, al. Mickiewicza 30, 30-059 Krakow, Poland)

  • Jarosław Wąs

    (Department of Applied Computer Science, Faculty of Electrical Engineering, Automatics, Computer Science and Biomedical Engineering, AGH University of Krakow, al. Mickiewicza 30, 30-059 Krakow, Poland)

Abstract

In the quest for sustainable energy solutions, predicting electricity prices for renewable energy sources plays a pivotal role in efficient resource allocation and decision making. This article presents a novel approach to forecasting electricity prices for renewable energy sources using deep learning models, leveraging historical data from the power system operator (PSE). The proposed methodology encompasses data collection, preprocessing, feature engineering, model selection, training, and evaluation. By harnessing the power of recurrent neural networks (RNNs) and other advanced deep learning architectures, the model captures intricate temporal relationships, weather patterns, and demand fluctuations that impact renewable energy prices. The study demonstrates the applicability of this approach through empirical analysis, showcasing its potential to enhance energy market predictions and aid in the transition to more sustainable energy systems. The outcomes underscore the importance of accurate renewable energy price predictions in fostering informed decision making and facilitating the integration of renewable sources into the energy landscape. As governments worldwide prioritize renewable energy adoption, this research contributes to the arsenal of tools driving the evolution towards a cleaner and more resilient energy future.

Suggested Citation

  • Michał Pikus & Jarosław Wąs, 2024. "Predictive Modeling of Renewable Energy Purchase Prices Using Deep Learning Based on Polish Power Grid Data for Small Hybrid PV Microinstallations," Energies, MDPI, vol. 17(3), pages 1-12, January.
  • Handle: RePEc:gam:jeners:v:17:y:2024:i:3:p:628-:d:1328300
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    References listed on IDEAS

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    1. Karsten Neuhoff & Jörn C. Richstein & Mats Kröger, 2023. "Reacting to Changing Paradigms: How and Why to Reform Electricity Markets," DIW Berlin: Politikberatung kompakt, DIW Berlin, German Institute for Economic Research, volume 127, number pbk189.
    2. Michal Pikus & Jarosław Wąs, 2023. "Using Deep Neural Network Methods for Forecasting Energy Productivity Based on Comparison of Simulation and DNN Results for Central Poland—Swietokrzyskie Voivodeship," Energies, MDPI, vol. 16(18), pages 1-15, September.
    3. Neuhoff, Karsten & Richstein, Jörn C. & Kröger, Mats, 2023. "Reacting to changing paradigms: How and why to reform electricity markets," Energy Policy, Elsevier, vol. 180(C).
    4. Lago, Jesus & De Ridder, Fjo & De Schutter, Bart, 2018. "Forecasting spot electricity prices: Deep learning approaches and empirical comparison of traditional algorithms," Applied Energy, Elsevier, vol. 221(C), pages 386-405.
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