Whether your challenge can be solved by our existing products or requires the development of new analytics solutions and market design approaches, our team is ready to help.
DateApril 28, 2022 |
LocationOnline Event |
Timing03:00 pm - 04:00 pm (CET) |
In the last few months, the evolution of electricity prices have pushed energy market participants to find newer, more innovative forecasting solutions. Being able to anticipate short-term electricity prices from day-ahead to real-time with the right level of accuracy, such us minimizing absolute errors, is no longer sufficient.
Electricity generators and producers, flexible asset owners, traders, and retailers are now looking for AI-powered solutions that will give them the data and insights they need to make market moves with more confidence in increasingly uncertain times.
During this webinar we'll show you how N-SIDE's AI software can deliver forecasted values along with their associated confidence levels, allowing you to make the best decisions possible.
With a background in Business Engineering and the exposure to various advanced analytics start-ups, Michael is passionate about the energy sector and the development of innovative solutions to accelerate the energy transition. After putting a strong focus on the development of N-SIDE Energy activities towards different types of actors, he now has specific expertise in energy forecasting solutions for market participants. Michael now leads the Energy Forecasting Platform team at N-SIDE.
With a background in physics and statistics, Pierre has built robust statistical analysis and numerical modeling skills, first in the high-energy physics academic community and then in the corporate energy sector. Besides his technical work, Pierre has produced high-quality reports (including international publications in prestigious journals) and presented talks on many occasions. At N-SIDE, Pierre led a technology suite of innovative algorithms for energy price & volume forecasts, and brought breakthroughs in the field of Explainable AI (XAI) to increase interpretability of trained models.
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