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Revolutionizing Renewable Energy: Advanced Synthetic Climatic Models for Enhanced Energy Predictability and Optimization
April 2 @ 1:15 pm - 1:40 pm CDT
2025 Spring IEEE OKC Webinar Series
IEEE OKC invites you to join the event below, organized by Oklahoma International Publishing, as part of its 2025 Spring OkIP Conferences on Wednesday, April 2, 2025.
This virtual event is free of charge to IEEE Oklahoma City members. Please register ahead of time to receive the proper instructions for remote participation:
>> Speaker(s):
Wulfran Fendzi Mbasso, CITRD Lab of Oklahoma City
>> Talk:
Revolutionizing Renewable Energy: Advanced Synthetic Climatic Models for Enhanced Energy Predictability and Optimization
>>> Abstract:
The efficacy of renewable energy systems rests heavily on solar irradiance and temperature. In reality, however, these data tend to be under or unavailable in certain areas, making accurate design, modeling, and analysis of renewable energy systems unfathomably problematic. This paper discusses the creation and evaluation of novel synthetic climatic models aimed at providing region-specific realistic climatic data. These models utilize highly advanced statistical and machine learning algorithms combined with solar irradiance and temperature models to capture and integrate solar irradiance and temperature data with relative high temporal and spatial resolution. Models’ extensive validation against real climatic datasets considerably increased their reliability and robustness under various conditions including extreme and less plausible scenarios. These results highlight the ability of the models to close the gap posed by the utter absence of reliable, region accurate data in supplementing renewable energies. Such findings are greatly beneficial for improving energy yield forecast, system design, and performance monitoring and evaluation for areas with scarce data resources.
Co-sponsored by: Pierre Tiako
Agenda:
01:20pm – 01:38pm Virtual Meeting
01:38pm- 01:40pm Virtual Meeting Q &A
Virtual: https://events.vtools.ieee.org/m/479232