Energy markets require agent-based modeling and AI for accurate forecasting

Energy markets, like the J&J market, function as complex adaptive systems with numerous agents and traders. These agents operate with changing decision rules reflecting their understanding of the market dynamics. To forecast accurately in such environments, a combination of different models that communicate with each other is essential. Integrating machine learning, a subset of AI, with simulation techniques enhances forecast accuracy by constructing various universes where agents can operate and adapt to changing conditions.