The forecasting of prices for electricity balancing reserve power can essentially improve the trading positions of market participants in competitive auctions. Having identified a lack of literature related to forecasting balancing reserve prices, we deploy approaches originating from econometrics and artificial intelligence and set up a forecasting framework based on autoregressive and exogenous factors. We use SARIMAX models as well as neural networks with different structures and forecast based on a rolling one‐step forecast with reestimation of the models. It turns out that the naive forecast performs reasonably well but is outperformed by the more advanced models. In addition, neural network approaches outperform the econometric approach in terms of forecast quality, whereas for the further use of the generated models the econometric approach has advantages in terms of explaining price drivers. For the present application, more advanced configurations of the neural networks are not able to further improve the forecasting performance.

中文翻译：

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利用计量经济学和人工智能对频率遏制底价进行建模

电力平衡储备电力的价格预测可以从根本上改善竞争性拍卖中市场参与者的交易状况。在发现缺乏与预测平衡储备价格有关的文献之后，我们采用源自计量经济学和人工智能的方法，并基于自回归和外生因素建立了一个预测框架。我们使用SARIMAX模型以及具有不同结构和神经网络的神经网络，并基于对模型进行重新估计的滚动式单步预测进行预测。事实证明，幼稚的预测效果相当好，但在更高级的模型中却不如预期。此外，在预测质量方面，神经网络方法优于计量经济学方法，而对于进一步使用生成的模型，计量经济学方法在解释价格动因方面具有优势。对于本申请，神经网络的更高级的配置不能进一步提高预测性能。