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WEBINAR


SHORT-TERM ELECTRICITY PRICES: HOW AI CAN HELP YOU BUILD CONFIDENCE IN THE
CURRENT MARKET VOLATILITY


DATE

April 28, 2022


LOCATION

Online Event


TIMING

03:00 pm - 04:00 pm (CET)

REGISTER NOW


CONTEXT

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. 




WHAT YOU WILL LEARN

 * How to optimise the usage of your flexible asset with AI models
 * How to increase the value of your portfolio on short-term markets with
   confidence while relying on machine learning models
 * How to keep visibility on the main fundamentals behind short-term electricity
   prices
 * Which technologies are best suited to build forecasts with sound confidence
   level estimates





SPEAKERS

Michael Malcorps Head of Energy Forecasting Product Management

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.


PIERRE ARTOISENET Energy Senior Expert

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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