## 5 Discussion
Another limitation when comparing the models based on the provided results is that all prediction steps have equal weight--a prediction error in the next time step is as critical to the models as the prediction error one week ahead. This error can be observed in scenarios 1 and 3, where no strictly monotonous behavior exists. Depending on the specific use case, using shorter prediction horizons and combining models with different prediction horizons might be beneficial. Predictions might become vague if the chosen forecast horizon is too long. Power plants capable of quick adjustments may benefit from shorter forecast periods as the difficulty of the modeling task decreases. Hence, the predictions become more accurate. Lastly, a possible quality criterion is the smoothness of the predictions. Even though the training setup is identical, there is no guarantee of smooth predictions, being spikey or erratic. This phenomenon might be due to the training data, as it can only be observed in test scenario 3. Nonetheless, regularization methods exist to ensure generalizability which can smoothen the predictions such as dropout or noise injection. Based on our investigations, this concept can be applied to any neural network to improve them further. With the application of regularization methods, overfitting is likely to be prevented as the random process of dropout and noise injection makes any training iteration unique. The constrained model becomes more robust as the training is more challenging. Comparing our investigated approaches for each scenario supports the global finding: A deep learning model predicts all scenarios most accurately. Since the scenarios are unique, each possesses a challenge that needs to be solved. A sharp decline within a seasonal pattern characterizes scenarios 1 and 3. Scenario 2 adds difficulty because it consists of a seasonal decrease and a heavy negative trend. The idea of the hybrid approach is to reduce the noise of the given input and to extract the most relevant features only. Here, filtering facilitates the process of keeping the focus on the relevant parts. The filtering finally enables closer predictions but also implies trade-offs. For example, it is possible that the smoothing effect becomes too strong and destroys crucial parts of the sequence. In that case, the LSTM will not be able to predict the original time series problem very accurately anymore [67]. Taking a step back and considering the problem more extensively, additional future research work can be identified. The overall goal is to build a robust energy-price prediction system that requires minimal human effort in operation. However, more comprehensive measures are needed to build an ML solution suitable for a power plant control software system incorporating cross-dependencies between data, models, code, and configurations [74]. Lastly, model explainability techniques [75], and uncertainty quantification [76] need to be incorporated to support the predicted results' reasoning well as gain trust and transparency for the high-impact decisions such a system facilitates. Using a dropout layer in a neural network during inference and repeat making forecasts multiple times is an approach to investigate uncertainty. By randomly shutting off neurons of a dense layer, the model gets into a situation where information gets discarded. This procedure forces the model to use all input features and not rely on some features only [77]. Conformal predictions can provide additional insights that increase confidence in the prediction model. This approach returns prediction intervals instead of points. The point estimation made by the prediction model is guaranteed to be within the interval given a high probability [78]. Both methods aim to reason predictions, so power plant dispatchers gain confidence and trust in the application. They will be part of future work helping to understand how a fluctuating day-ahead electricity price causes forecast uncertainty.