## 5 Discussion  
Table 1 shows that LSTM models, on average, predict the energy market prices with the highest accuracy in all three defined scenarios. This result is consistent with findings of several publications using training and validation data prior to 2020 [15; 16; 17; 18; 19]. LSTM models are suggested to be especially suitable for handling non-linear, complex dependencies over time. The energy market is a prime example due to its volatility and highly distinctive seasonal characteristics [10; 11]. Unforeseeable exogenous events (e.g., pandemics, wars) complicate predictions even further and thus make it difficult to accurately predict price jumps multiple days ahead, as these market-changing events are not present as input features. While allegedly simpler algorithms seem not to be powerful enough to capture the highly dynamic, non-linear stochastic nature of energy prices, neural networks with their flexible mechanisms, for example, to learn what parts of history to'remember' and what to 'forget' in a given sequence, tend to be more appropriate as they are specifically designed to solve such kind of problems [65]. A second criterion is model robustness because a more robust model can be expected to be less sensitive to changes in the data distribution over time. A robust model is given if the model goodness deviates only slightly when hyperparameters change. This does not include generalizability, where training and validation errors are tightly coupled. Model robustness explains how much the predictions change depending on which hyperparameter and validation data are selected. The results  
Figure 11: LSTM prediction for test scenario 2. Figure 12: LSTM prediction for test scenario 3.  
also show that LSTM models work well on raw time-series data, simplifying the modeling pipeline and reducing the degrees of freedom that need to be considered during model training. However, some authors recommend variance stabilizing transformations [72; 73] or outlier detection to remove price spikes [14]. In order to see how our models deal with these spikes and how they perform, we deliberately chose not to use any of these correcting methods. In the given context of power plant control operations, sharp price spikes are part of the signal. They are particularly interesting for energy technologies that can reach high dynamics (e.g., power to heat, controllable loads). The results also show that among the other model types, ARIMA requires the data set to be stationary [41]. There needs to be a consensus about using static data for the remaining machine learning models, just weak indications of the raw data. In the German and European energy markets, it has become common for market participants to base their decisions on forecasts in recent years. Due to the ratio of market participants and volumes traded, it is doubtful that a forecast will give any single market participant a decisive advantage. This applies to suppliers as well as buyers and network operators. This leads to a better balance between supply and demand and reduced dispatch costs. The practical results of this study are currently being tested by dispatchers in the Power Plant Dispatching Department and have a weighted influence on decision-making in power plant operation. The forecasts are used in addition to forecasts from third parties, thus increasing the data basis for decisions. Future developments should focus on explainability to answer questions like \"why fly ups/downs occur?\" \"how much is each predictor going to price?\" or \"are my predictors plausible?\". One limitation of the presented experimental results is the model evaluation metric. Other possible metrics indicate robustness (e.g., MAPE, DAE, and normalized variants of commonly used metrics [14]). However, \(RMSE\) is the most suitable metric, as it penalizes outliers quadratically. Furthermore, \(RMSE\) is valuable for its ease of interpretation due to unit conservation, here EUR/MWh.