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Using Deep Neural Network Methods for Forecasting Energy Productivity Based on Comparison of Simulation and DNN Results for Central Poland—Swietokrzyskie Voivodeship

Author

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  • Michal Pikus

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

  • Jarosław Wąs

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

Abstract

Forecasting electricity demand is of utmost importance for ensuring the stability of the entire energy sector. However, predicting the future electricity demand and its value poses a formidable challenge due to the intricate nature of the processes influenced by renewable energy sources. Within this piece, we have meticulously explored the efficacy of fundamental deep learning models designed for electricity forecasting. Among the deep learning models, we have innovatively crafted recursive neural networks (RNNs) predominantly based on LSTM and combined architectures. The dataset employed was procured from a SolarEdge designer. The dataset encompasses daily records spanning the past year, encompassing an exhaustive collection of parameters extracted from solar farm (based on location in Central Europe (Poland Swietokrzyskie Voivodeship)). The experimental findings unequivocally demonstrated the exceptional superiority of the LSTM models over other counterparts concerning forecasting accuracy. Consequently, we compared multilayer DNN architectures with results provided by the simulator. The measurable results of both DNN models are multi-layer LSTM-only accuracy based on R2—0.885 and EncoderDecoderLSTM R2—0.812.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:18:p:6632-:d:1240507
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    References listed on IDEAS

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    1. Bogdan Bochenek & Jakub Jurasz & Adam Jaczewski & Gabriel Stachura & Piotr Sekuła & Tomasz Strzyżewski & Marcin Wdowikowski & Mariusz Figurski, 2021. "Day-Ahead Wind Power Forecasting in Poland Based on Numerical Weather Prediction," Energies, MDPI, vol. 14(8), pages 1-18, April.
    2. Dariusz Gołȩbiewski & Tomasz Barszcz & Wioletta Skrodzka & Igor Wojnicki & Andrzej Bielecki, 2022. "A New Approach to Risk Management in the Power Industry Based on Systems Theory," Energies, MDPI, vol. 15(23), pages 1-19, November.
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    Cited by:

    1. 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.
    2. Zhiqiang Peng & Jichong Lei & Zining Ni & Tao Yu & Jinsen Xie & Jun Hong & Hong Hu, 2024. "Research on Data-Driven Methods for Solving High-Dimensional Neutron Transport Equations," Energies, MDPI, vol. 17(16), pages 1-11, August.

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    Keywords

    AI; forecasting; LSTM; DNN; PV;
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