Bridging an energy system model with an ensemble deep-learning approach for electricity price forecasting

TL;DR

This paper presents an integrated approach combining a techno-economic energy system model with an ensemble deep learning method to significantly improve electricity price forecasting accuracy and economic value in the German market.

Contribution

It introduces a novel combined model that enhances forecast accuracy by 18% and demonstrates its economic benefits for storage revenue optimization.

Findings

Integrated model improves forecasting accuracy by 18%.

Ensemble Deep Neural Network performs best among tested models.

Adding energy system model outputs enhances econometric model performance.

Abstract

This paper combines a techno-economic energy system model with an econometric model to maximise electricity price forecasting accuracy. The proposed combination model is tested on the German day-ahead wholesale electricity market. Our paper also benchmarks the results against several econometric alternatives. Lastly, we demonstrate the economic value of improved price estimators maximising the revenue from an electric storage resource. The results demonstrate that our integrated model improves overall forecasting accuracy by 18 %, compared to available literature benchmarks. Furthermore, our robustness checks reveal that a) the Ensemble Deep Neural Network model performs best in our dataset and b) adding output from the techno-economic energy systems model as econometric model input improves the performance of all econometric models. The empirical relevance of the forecast improvement is confirmed by the results of the exemplary storage optimisation, in which the integration of the techno-economic energy system model leads to a revenue increase of up to 10 %.