In the context of the European energy market, the role of electricity price forecasts is becoming increasingly pronounced. This is due to the high volatility of electricity prices, which is a consequence of the increasing share of renewable energies, such as wind and solar power, in the energy mix. At the same time, artificial intelligence (AI) offers novel possibilities for mapping and forecasting these fluctuations. In contrast, conventional methods of electricity price forecasting (e.g., regression analysis) need to be evaluated in relation to artificial intelligence methods with respect to their performance within the context of the modified framework conditions.
The objective of this bachelor's thesis is to conduct a structured literature analysis that systematically examines the use of AI in electricity price forecasting in the European context and compares them with conventional forecasting methods.
Basic knowledge in the field of artificial intelligence is advantageous.
If you are interested, please contact Christian Scheller.