Skip to main content Skip to main navigation menu Skip to site footer
From the Editor-in-Chief
DOI: 10.14254/1795-6889.2024.21-3.0
Submitted: 2025-10-09
Published: 2025-12-30

Ecological and secure electricity microgrids - monitoring and forecasting challenges

Warsaw School of Economics
Lodz University of Technology
University of Debrecen, Hungary; LUT School of Engineering Sciences, University in Lappeenranta, Finland
energy anomaly detection forecast modelling microgrids cybersecurity

Abstract

Reliable, low-carbon power systems depend on high-granularity, short-horizon forecasting, especially within islandable microgrids where consumers and prosumers shape real-time balance. Ultra-short-term (15-minute) forecasts support storage scheduling, demand response, and secure operation amid renewable intermittency and growing cyber risk. Forecasting approaches are classified into black-box, gray-box, and white-box methods, highlighting trade-offs in accuracy, explainability, and deployability. Operational use-cases are aligned with forecasting time scales, and current literature shows notable gaps: limited 15-minute multi-step studies, inconsistent evaluation protocols, insufficient attention to explainability, and restricted access to representative datasets. Design principles are outlined for deployable forecasting and control: standardised metrics and horizons, privacy-preserving data pipelines, explainability-first modelling, and transferable domain-specific hyperparameters. Addressing these gaps can increase renewable penetration, lower imbalance costs, strengthen cybersecurity compliance, and enhance resilience, delivering cleaner energy at reduced cost with higher quality of service. Researchers who want to support effective energy management technologies with their research should be aware of the current challenges.

Metrics

Metrics Loading ...

References

  1. Amasyali, K., & El-Gohary, N. M. (2018). A review of data-driven building energy consumption prediction studies. Renewable and Sustainable Energy Reviews, 81, 1192–1205. https://doi.org/10.1016/j.rser.2017.04.095 DOI: https://doi.org/10.1016/j.rser.2017.04.095
  2. Cao, W., Yu, J., Chao, M., Wang, J., Yang, S., Zhou, M., & Wang, M. (2023). Short-term energy consumption prediction method for educational buildings based on model integration. Energy, 283, 128580. https://doi.org/10.1016/j.energy.2023.128580 DOI: https://doi.org/10.1016/j.energy.2023.128580
  3. Demir, E., & Gunal, S. (2025). Short-term electricity consumption forecasting with deep learning. The Journal of Supercomputing, 81(10), 1108. https://doi.org/10.1007/s11227-025-07564-5 DOI: https://doi.org/10.1007/s11227-025-07564-5
  4. European Parliament, & Council of the European Union. (2022). Directive (EU) 2022/2555 on measures for a high common level of cybersecurity across the Union (NIS 2 Directive). Official Journal of the European Union, L333, 80–152. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A32022L2555
  5. European Commission. (2023). Proposal for a regulation on horizontal cybersecurity requirements for products with digital elements (Cyber Resilience Act). COM/2022/454 final. https://eur-lex.europa.eu/legal-content/EN/TXT/?uri=CELEX%3A52022PC0454
  6. Klyuev, R. V., Morgoev, I. D., Morgoeva, A. D., Gavrina, O. A., Martyushev, N. V., Efremenkov, E. A., & Mengxu, Q. (2022). Methods of forecasting electric energy consumption: A literature review. Energies, 15(23), 8919. https://doi.org/10.3390/en15238919 DOI: https://doi.org/10.3390/en15238919
  7. Mukhtarov, S., Aliyev, J., Borowski, P.F., & Disli, M. (2023). Institutional quality and renewable energy transition: Empirical evidence from Poland. Journal of International Studies, 16(3), 208-218. doi:10.14254/2071-8330.2023/16-3/12 DOI: https://doi.org/10.14254/2071-8330.2023/16-3/12
  8. Olu-Ajayi, R., Alaka, H., Owolabi, H., Akanbi, L., & Ganiyu, S. (2023). Data-driven tools for building energy consumption prediction: A review. Energies, 16(6), 2574. https://doi.org/10.3390/en16062574 DOI: https://doi.org/10.3390/en16062574
  9. Somu, N., Radhika, G. M., & Ramamritham, K. (2021). A deep learning framework for building energy consumption forecast. Renewable and Sustainable Energy Reviews, 137, 110591. https://doi.org/10.1016/j.rser.2020.110591 DOI: https://doi.org/10.1016/j.rser.2020.110591
  10. Vasylieva, T., Derkacz, A., Popp, J., & Horsch, A. (2025). From energy dependency to energy security: How the war in Ukraine accelerated renewable deployment in Europe. Economics and Sociology, 18(3), 229-253. doi:10.14254/2071-789X.2025/18-3/13 DOI: https://doi.org/10.14254/2071-789X.2025/18-3/13
  11. Wang, Z., & Srinivasan, R. S. (2017). A review of artificial intelligence based building energy use prediction: Contrasting the capabilities of single and ensemble prediction models. Renewable and Sustainable Energy Reviews, 75, 796–808. https://doi.org/10.1016/j.rser.2016.10.079 DOI: https://doi.org/10.1016/j.rser.2016.10.079

How to Cite

Wojciechowski, W., Niewiadomski, A., & Bilan, Y. (2025). Ecological and secure electricity microgrids - monitoring and forecasting challenges. Human Technology, 21(3), 469–473. https://doi.org/10.14254/1795-6889.2024.21-3.0