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Long-Term Forecast of Peak Power Demand for Poland—Construction and Use of Simplified Forecasting Models

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  • Tomasz Popławski

    (Department of Electrical Engineering, Czestochowa University of Technology, 42-200 Czestochowa, Poland)

Abstract

This article presents a simplified method for forecasting Poland’s long-term peak electricity demand using a modified Prigogine logistic equation. While complex models like the WEM or PRIMES offer high precision, their complexity and data requirements can be limiting. The proposed model offers a quicker and more accessible alternative, using the average annual load factor ( A L F ) as a key indicator. Based on historical data (1985–2024), the model was validated and optimized (MAPE < 2%), then applied to forecast the demand through 2040 under three scenarios: coal-based energy, nuclear energy and energy from RESs (renewables). Depending on the scenario, the peak demand is expected to rise from 28.7 GW in 2024 to 34–40 GW in 2040. The model’s strength lies in its ability to capture dynamic system behavior, including chaos and bifurcations, making it suitable for rapid assessments and strategic planning. Despite its limitations—such as a lower level of detail and an inability to integrate sectoral policies—the Prigogine-based approach offers a transparent, cost-effective forecasting tool, especially when complemented by the use of advanced simulation models.

Suggested Citation

  • Tomasz Popławski, 2025. "Long-Term Forecast of Peak Power Demand for Poland—Construction and Use of Simplified Forecasting Models," Energies, MDPI, vol. 18(13), pages 1-17, July.
    • Handle: RePEc:gam:jeners:v:18:y:2025:i:13:p:3472-:d:1692476
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    References listed on IDEAS

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    1. Jing Xie, 2023. "Identifying Optimal Indicators and Lag Terms for Nowcasting Models," IMF Working Papers 2023/045, International Monetary Fund.
    2. Martin Anokye & Elvis Kobina Donkoh & Michael Fosu Ofori & Agnes Adom-Konadu & Mariano Torrisi, 2022. "Full Delay Logistic Population Model with Sustainable Harvesting," Journal of Applied Mathematics, Hindawi, vol. 2022, pages 1-9, August.
    3. Francis Magloire Peujio Fozap, 2025. "Hybrid Machine Learning Models for Long-Term Stock Market Forecasting: Integrating Technical Indicators," JRFM, MDPI, vol. 18(4), pages 1-21, April.
    4. Martin Anokye & Elvis Kobina Donkoh & Michael Fosu Ofori & Agnes Adom-Konadu, 2022. "Full Delay Logistic Population Model with Sustainable Harvesting," Journal of Applied Mathematics, John Wiley & Sons, vol. 2022(1).
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