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How good are TSO load and renewable generation forecasts: Learning curves, challenges, and the road ahead

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  • Kazmi, Hussain
  • Tao, Zhenmin

Abstract

Transmission system operators (TSOs) forecast load and renewable energy generation to maintain smooth functioning of the grid by contracting sufficient generation and reserve capacity. These forecasts are also utilized by third parties, such as energy generators and demand aggregators, in their own forecasting and decision-making pipelines e.g. to determine suitable trading strategies. Inaccurate forecasts by the TSOs can therefore lead to increased balancing needs as well as elevated societal and market costs. The situation is further exacerbated by the challenges arising due to rapidly increasing renewable generation and the effects of the post-Covid era. In this paper, we analyse five years of TSO forecasts for load, wind and solar generation for 16 European countries. More concretely, using a comprehensive set of metrics, we explore relevant questions such as whether there are TSO specific differences in forecast accuracy, and how forecast errors have changed over time and if they can be reduced further. Our results show that while errors tend to increase linearly with demand or renewable generation, most TSOs still have considerable room for improvement in terms of accuracy. The paper concludes with a set of recommendations for TSOs to improve their forecasts, as well as the ENTSO-E transparency platform where we obtained the data used in this study.

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  • Kazmi, Hussain & Tao, Zhenmin, 2022. "How good are TSO load and renewable generation forecasts: Learning curves, challenges, and the road ahead," Applied Energy, Elsevier, vol. 323(C).
    • Handle: RePEc:eee:appene:v:323:y:2022:i:c:s0306261922008753
    DOI: 10.1016/j.apenergy.2022.119565
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    2. Stefanos Tampakakis & Dimitrios Zafirakis, 2023. "On the Value of Emerging, Day-Ahead Market Related Wind-Storage Narratives in Greece: An Early Empirical Analysis," Energies, MDPI, vol. 16(8), pages 1-19, April.
    3. van Zyl, Corne & Ye, Xianming & Naidoo, Raj, 2024. "Harnessing eXplainable artificial intelligence for feature selection in time series energy forecasting: A comparative analysis of Grad-CAM and SHAP," Applied Energy, Elsevier, vol. 353(PA).
    4. Kazmi, Hussain & Mehmood, Fahad & Shah, Maryam, 2024. "Quantifying residential energy flexibility potential for demand response programs using observational data from grid outages: Evidence from Pakistan," Energy Policy, Elsevier, vol. 188(C).
    5. Dimitrios Kontogiannis & Dimitrios Bargiotas & Athanasios Fevgas & Aspassia Daskalopulu & Lefteri H. Tsoukalas, 2024. "Combinatorial Component Day-Ahead Load Forecasting through Unanchored Time Series Chain Evaluation," Energies, MDPI, vol. 17(12), pages 1-46, June.
    6. Houben, Nikolaus & Cosic, Armin & Stadler, Michael & Mansoor, Muhammad & Zellinger, Michael & Auer, Hans & Ajanovic, Amela & Haas, Reinhard, 2023. "Optimal dispatch of a multi-energy system microgrid under uncertainty: A renewable energy community in Austria," Applied Energy, Elsevier, vol. 337(C).

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