LSTM-Based Forecasting of Danish Electricity Imbalance Price: A Comparative Study of Classical and Machine Learning Models

LSTM-Based Forecasting of Danish Electricity Imbalance Price

Christiansen, Christian Taulbjerg Gørup

4. term

Mathematics-Economics, Master

2025

2025-05-28

42

Forecasting imbalance prices in electricity markets has become increasingly relevant due to volatility in part from renewable integration and regulatory reforms. This thesis explores the feasibility of modelling the Danish DK1 balancing market using LSTM, with the goal of enabling flexible assets to act after the closure of the ID market. A stateful LSTM model was trained under cuDNN constraints utilized GPU acceleration, mixed precision training, and memory optimization to support a hyperparameter tuning process that would otherwise have been computationally prohibitive. The tuning itself was performed using a combination of Hyperband and Bayesian Optimization. The LSTM model was benchmarked against ARIMA, naive baselines, and methods such as Random Forest and XGBoost. Results showed consistent improvements in predictive accuracy after transforming the target variable through co-integration (assuming unit root) with the day ahead spot price, capturing the structural anchoring relationship and improving the signal to noise ratio. Despite the complexity of LSTMs, simple AR models performed surprisingly well after transformation, underlining the short term dynamics that dominate the imbalance market. Ultimately, the optimized LSTM model demonstrated strong performance in capturing extreme price spikes and directional shifts, opening for post ID operations. The thesis highlight the importance of aligning model design with market structure and how simple models, under the right conditions can serve as an alternative.

LSTM ; ML ; Cointegration ; Energi market ; Spot price ; Imbalance price ; mFRR ; aFRR ; Random Forrest ; XGboost ; Hyperband ; Bayesian Optimization

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