![]() In addition, there are well-documented and publicly available data sets for this market that can be used. 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. 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. Price jumps of several hundred EUR/MWh can be observed. In addition, day-ahead volatility has, at times, increased many times. The price trend has increased over time, and the day-ahead price fluctuations increased firmly along. Each scenario corresponds to a different market behavior. Each scenario uses different training and test sets with relatively few samples. We consider these requirements by using a scenario based approach. Instead, prediction methods are needed that work with limited historical data. ![]() Changes in the market mechanisms and the increased volatility invalidates most of the historical data. Furthermore, the market is more and more volatile and reacts increasingly sensitively to political, social, and secondary events, which are reflected in immediate trends (see Figure 1). However, the relevant time-frame for production planning in the range of several days is hardly considered in the research on energy price predictions. 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, 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. It allows operators to adjust to energy shortages or overproduction and accommodate the prioritized renewable energies into the grid. This dispatching approach is required to regulate the power grid. They allow reacting to supply and demand changes early by reserving generating capacity or shutting down power plant units. This study shows that deep learning models outperform tree-based and statistical models despite or because of the volatile energy prices.Īccurate energy market forecasts are increasingly important for power plant operators and other energy suppliers. Various models are trained, optimized, and compared with each other using common performance metrics. For evaluation purposes, three test scenarios with different characteristics are manually chosen. A qualitative and quantitative analysis is conducted, assessing model performance concerning the forecast horizon and their robustness depending on the selected hyperparameters. This study investigates the application of machine and deep learning approaches to predict day-ahead electricity prices for a 7-day horizon on the German spot market to give power plants enough time to ramp up or down. This development makes ML models an attractive complement to other approaches that require more extensive human modeling effort and assumptions about market mechanisms. Machine learning (ML) has recently emerged as a powerful artificial intelligence (AI) technique to get reliable predictions in particularly volatile and unforeseeable situations. This uncertain market trend behavior makes it harder for market participants, e.g., power plant dispatchers, to make reliable decisions. ![]() In recent years, energy prices have become increasingly volatile, making it more challenging to predict them accurately. ![]()
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