Energy Trading with Machine Learning and Forecasting Models

Course overview

Energy Trading with Machine Learning and Forecasting Models 1. Introduction to Energy Trading and Machine Learning: Fundamentals of energy trading and its market structure, importance of machine learning in energy trading, overview of use cases and technologies 2. Data Analysis in Energy Trading: Techniques for data preparation and analysis in energy trading, use of machine learning algorithms for forecasting, best practices for identifying trading opportunities 3. Practical Application of Machine Learning in Energy Trading: Case studies of successful machine learning implementations, exercises for applying ML models to energy data, development of a prototype for a trading forecast 4. Advanced Techniques in Energy Trading: Integration of deep learning in energy forecasts, optimization of trading strategies with advanced analytics, success stories from the industry 5. Management of Energy Trading Projects: Roles and responsibilities in the energy trading team, strategies for implementing energy trading projects, risk and change management in energy trading 6. Measuring the Success of Machine Learning in Energy Trading: KPIs for evaluating the efficiency of trading forecasts, methods for analyzing ROI in energy trading projects, optimization of projects based on results 7. Practical Project for Developing an Energy Trading Forecast Model: Development and presentation of a personal energy trading project, feedback session with other participants, documentation and reflection on the results 8. Future of Energy Trading with Machine Learning: Trends and developments in energy trading and machine learning, potentials for new business models in energy trading, strategies for continuous improvement of forecasting models

Upcoming dates

• 2026-10-05T22:00:00.000Z · 2027-05-17T22:00:00.000Z · Virtuelles Klassenzimmer
• 2026-10-05T22:00:00.000Z · 2027-12-01T23:00:00.000Z · Virtuelles Klassenzimmer
• 2026-10-05T22:00:00.000Z · 2027-05-17T22:00:00.000Z · München · Combined Learning
• 2026-10-05T22:00:00.000Z · 2027-12-01T23:00:00.000Z · München · Combined Learning
• 2026-11-15T23:00:00.000Z · 2027-06-27T22:00:00.000Z · Virtuelles Klassenzimmer