Speaker
Description
Along with the growing share of renewable energy sources together with pan European integration of day-ahead and intraday electricity markets, volatility in all kind of energy markets has been increasing in recent years. Increased liquidity in intraday markets helps to minimize deviations and stabilize the transmission system, and in long-term markets it helps to hedge contracts. With this increasing number of trades, market participants, and huge amount of data, the possibility to use machine learning models and neural networks for trading also comes into play. This text focuses on the research of these models and their implementation in energy markets. The aim is to process available data from energy markets and weather data from Europe and to explore possible correlations. The next goal is to prepare and train a robust model and verify it on recent data. The implementation of a solution for time series on algorithmic trading is an upcoming task of this work.
