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Machine Learning- and Artificial Intelligence-Derived Prediction for Home Smart Energy Systems with PV Installation and Battery Energy Storage

Author

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  • Izabela Rojek

    (Faculty of Computer Science, Kazimierz Wielki University, Chodkiewicza 30, 85-064 Bydgoszcz, Poland)

  • Dariusz Mikołajewski

    (Faculty of Computer Science, Kazimierz Wielki University, Chodkiewicza 30, 85-064 Bydgoszcz, Poland)

  • Adam Mroziński

    (Faculty of Mechanical Engineering, Bydgoszcz University of Science and Technology, Kaliskiego 7, 85-796 Bydgoszcz, Poland)

  • Marek Macko

    (Faculty of Mechatronics, Kazimierz Wielki University, Chodkiewicza 30, 85-064 Bydgoszcz, Poland)

Abstract

Overview: Photovoltaic (PV) systems are widely used in residential applications in Poland and Europe due to increasing environmental concerns and fossil fuel energy prices. Energy management strategies for residential systems (1.2 million prosumer PV installations in Poland) play an important role in reducing energy bills and maximizing profits. Problem: This article aims to check how predictable the operation of a household PV system is in the short term—such predictions are usually made 24 h in advance. Methods: We made a comparative study of different energy management strategies based on a real household profile (selected energy storage installation) based on both traditional methods and various artificial intelligence (AI) tools, which is a new approach, so far rarely used and underutilized, and may inspire further research, including those based on the paradigm of Industry 4.0 and, increasingly, Industry 5.0. Results: This paper discusses the results for different operational scenarios, considering two prosumer billing systems in Poland (net metering and net billing). Conclusions: Insights into future research directions and their limitations due to legal status, etc., are presented. The novelty and contribution lies in the demonstration that, in the case of domestic PV grids, even simple AI solutions can prove effective in inference and forecasting to support energy flow management and make it more predictable and efficient.

Suggested Citation

  • Izabela Rojek & Dariusz Mikołajewski & Adam Mroziński & Marek Macko, 2023. "Machine Learning- and Artificial Intelligence-Derived Prediction for Home Smart Energy Systems with PV Installation and Battery Energy Storage," Energies, MDPI, vol. 16(18), pages 1-26, September.
    • Handle: RePEc:gam:jeners:v:16:y:2023:i:18:p:6613-:d:1239524
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    References listed on IDEAS

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    Cited by:

    1. Izabela Rojek & Adam Mroziński & Piotr Kotlarz & Marek Macko & Dariusz Mikołajewski, 2023. "AI-Based Computational Model in Sustainable Transformation of Energy Markets," Energies, MDPI, vol. 16(24), pages 1-26, December.
    2. Kinga Stecuła & Radosław Wolniak & Wieslaw Wes Grebski, 2023. "AI-Driven Urban Energy Solutions—From Individuals to Society: A Review," Energies, MDPI, vol. 16(24), pages 1-34, December.
    3. Wang, Guimei & Mukhtar, Azfarizal & Moayedi, Hossein & Khalilpoor, Nima & Tt, Quynh, 2024. "Application and evaluation of the evolutionary algorithms combined with conventional neural network to determine the building energy consumption of the residential sector," Energy, Elsevier, vol. 298(C).
    4. Mokhtar Jlidi & Oscar Barambones & Faiçal Hamidi & Mohamed Aoun, 2024. "ANN for Temperature and Irradiation Prediction and Maximum Power Point Tracking Using MRP-SMC," Energies, MDPI, vol. 17(12), pages 1-21, June.

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