From day-ahead to mid and long-term horizons with econometric electricity price forecasting models

被引:1
作者
Ghelasi, Paul [1 ,2 ]
Ziel, Florian [1 ,2 ]
机构
[1] Univ Duisburg Essen, Data Sci Energy & Environm, Essen, Germany
[2] Univ Duisburg Essen, Chair Data Sci Energy & Environm House Energy Mark, Essen, Germany
关键词
Electricity price forecasting; Mid-term; Long-term; Renewables; Load; Econometric models; Unit root; Spurious effects; Regularization; SPOT-PRICES; POWER; CURVES; ENERGY;
D O I
10.1016/j.rser.2025.115684
中图分类号
X [环境科学、安全科学];
学科分类号
08 ; 0830 ;
摘要
Robust econometric models for mid to long-term electricity price forecasting have become increasingly critical due to evolving market dynamics and price volatility. The European energy crisis in 2022 led to unprecedented fluctuations in energy prices, underscoring challenges for operational and risk management. After a comprehensive literature analysis, we address key challenges: (1) Constraining coefficients with bounds derived from fundamental models for interpretability; (2) Incorporating seasonal expectations of regressors such as load and renewables to stabilize long-term forecasts; (3) Managing unit root behaviour of power prices by estimating same-day relationships and projecting them forward. We develop interpretable models for forecasting horizons from one day to one year and provide guidelines on modelling frameworks and key variables. A practical application with scenario analysis demonstrates the framework. We conduct forecasting studies on Germany's hourly electricity prices, by applying regularized regression and generalized additive models.
引用
收藏
页数:24
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