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SUMMARY:Forecasting Renewable Energy Production Using Machine Learning
DTSTART;VALUE=DATE-TIME:20260710T094000Z
DTEND;VALUE=DATE-TIME:20260710T103000Z
DTSTAMP;VALUE=DATE-TIME:20260703T161443Z
UID:indico-contribution-1160@events.grnet.gr
DESCRIPTION:Speakers: Anestis  Ampatzidis (EY & AUTH)\nThis presentation f
 ocuses on a practical application of Machine Learning and Deep Learning in
  renewable energy forecasting. Through Day-Ahead forecasting of solar ener
 gy production\, the discussion covers the complete data pipeline\, detaili
 ng how meteorological and historical records are processed to train predic
 tive ML models. Particular emphasis is placed onDeep Learning approaches s
 uch as Neural Networks. Additionally\, the role of Aggregators (FOSE) in t
 he modern energy market is examined to emphasize the real-world value of a
 ccurate forecasting. Finally\, a first version of an intuitive User Interf
 ace\, currently under development to manage the entire application pipelin
 e\, is presented\, offering a complete end-to-end perspective from raw dat
 a to the final user experience. The presentation also includes a hands-on 
 demonstration\, showcasing how a pre-trained Deep Learning model is used t
 o perform inference and generate solar energy production forecasts.\n\nhtt
 ps://events.grnet.gr/event/215/contributions/1160/
LOCATION:
URL:https://events.grnet.gr/event/215/contributions/1160/
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