标题： 将能源系统模型与集成深度学习方法相结合用于电力价格预测 显示英文标题

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

摘要： 本文将技术经济能源系统模型与计量经济学模型相结合，以最大化电力价格预测的准确性。 所提出的组合模型在德国日前批发电力市场进行测试。 我们的论文还将结果与其他几种计量经济学替代方案进行了比较。 最后，我们展示了改进的价格估计器的经济价值，这些估计器最大化了电力存储资源的收入。 结果表明，与现有文献基准相比，我们的综合模型整体预测准确性提高了18%。 此外，我们的稳健性检验结果表明，a) 集成深度神经网络模型在我们的数据集中表现最佳，b) 将技术经济能源系统模型的输出作为计量经济学模型输入可以提高所有计量经济学模型的性能。 通过示例存储优化的结果，证实了预测改进的实证相关性，在该优化中，集成技术经济能源系统模型导致收入最多增加了10%。 显示英文摘要

摘要： 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 %.