## 1 Introduction  
Accurate energy market forecasts are increasingly important for power plant operators and other energy suppliers. They allow reacting to supply and demand changes early by reserving generating capacity or shutting down power plant units. This dispatching approach is required to regulate the power grid. It allows operators to adjust to energy shortages or overproduction and accommodate the prioritized renewable energies into the grid. However, large industrial consumers are also increasingly interested in linking their demand to the price signal, which enables them to respond to price fluctuations and optimize electricity-intensive production costs. The need for reliable forecasts on the energy market is more important than ever due to the developments in exchange prices since October 2021 and the growing share of prioritized renewable energy sources. However, the relevant time-frame for production planning in the range of several days is hardly considered in the research on energy price predictions [1]. Furthermore, the market is more and more volatile and reacts increasingly sensitively to political, social, and secondary events [2; 3; 4; 5; 6; 7], which are reflected in immediate trends (see Figure 1). Changes in the market mechanisms and the increased volatility invalidates most of the historical data. Instead, prediction methods are needed that work with limited historical data. We consider these requirements by using a scenario based approach. Each scenario uses different training and test sets with relatively few samples. Each scenario corresponds to a different market behavior. The price trend has increased over time, and the day-ahead price fluctuations increased firmly along. In addition, day-ahead volatility has, at times, increased many times. Price jumps of several hundred EUR/MWh can be observed. Since these market scenarios have been unseen in the German market so far, with little data available, it is challenging to build robust models that reliably predict short-term prices for the next seven days. The German energy market is particularly interesting for this study as it has a unique, constantly changing market environment due to the predetermined exit path of conventional power plants and a very regionally specific increase in the number of renewable power plants. In addition, there are well-documented and publicly available data sets for this market that can be used. It is also interesting for the interpretation of the forecasts to be able to explain the occurrence of extreme values or to be able to evaluate the failure to predict specific events retroactively accurately. There exist many different approaches for the prediction of energy prices. The following subsection introduces a derived taxonomy of multiple methods used in this work. On a very high level, we can separate approaches based on explicit modeling and data-driven methods. Explicitly modeling means that human experts model the market dynamics, behavior of market participants, and physical relationships based on assumptions. On the other hand, data-driven models are entirely derived from historical data utilizing recent advances in machine learning, especially in deep learning. Hence, machine learning has become very attractive for energy price predictions. Data-driven models can complement explicit, human-in-the-loop models and serve these experts to validate their assumptions for their modeling approaches. However, they can also be used as an independent alternative. This work compares standard regression models that can be produced with popular ML modeling frameworks for predicting day-ahead electricity prices on the German Power Exchange. The model selection includes LSTM, CNN-LSTM, ARIMA, decision tree, random forest, gradient boosting tree, k-nearest-neighbor, support vector machines, and a Naive forecaster. This study aims to find a robust standard AI model for forecasting day-ahead prices in a highly volatile and changing market environment. This study is divided into 6 sections.