Integrating photo voltaic and wind energy into the prevailing electrical grid. Credit: Big Data and Cognitive Computing (2024). DOI: 10.3390/bdcc8030023
The continued transition from fossil fuels to renewable vitality sources has by no means been extra vital as local weather change and consciousness of sustainability proceed to rise.
This transition requires a powerful info construction to make sure a clean transition. Wind and solar energy is among the most ample sources of renewable vitality; nevertheless, engineers and researchers on this subject want a longtime information pipeline to successfully combine photo voltaic and wind energy into their course of design.
Abdullah Al-Aboosi is an interdisciplinary doctoral scholar within the Department of Multidisciplinary Engineering. He labored with Dr. Aldo Jonathan Muñoz Vazquez, a multidisciplinary engineering professor on the Higher Education Center in McAllen, of a neural community they hope will present such a pipeline.
The cause for this analysis resulted from a dialogue between Munoz Vazquez and Al-Aboosi. The thought developed right into a complete mission, drawing on the experience of varied collaborators, together with Wei Zhan, Dr. Mahmoud El-Halwagi and Dr. Fadhil Al-Aboosi with the RAPID Manufacturing Institute for Process Intensification. The RAPID Manufacturing Institute offers a precious open-access supply for correct information that researchers use to validate their mannequin predictions.
In phrases of the neural community itself, it may be used to supply an correct overview of any renewable system’s operation and life cycle evaluation by predicting the every day and hourly wind pace and photo voltaic irradiance. By precisely predicting the provision of photo voltaic and wind vitality, technological assets and provides may be managed extra effectively.
Al-Aboosi hopes that this mission will higher place renewable vitality as the primary supply of electrical energy within the industrial sector. The mission goals to allow researchers and renewable vitality set up corporations to find out the optimum variety of photo voltaic panels and wind generators wanted to stop extra or underproduction. Such a discount might encourage potential buyers to undertake this know-how and push for a future with cleaner air and greener electrical energy.
This analysis was revealed in Big Data and Cogitative Computing Journal.
More info:
Abdullah F. Al-Aboosi et al, Solar and Wind Data Recognition: Fourier Regression for Robust Recovery, Big Data and Cognitive Computing (2024). DOI: 10.3390/bdcc8030023
Provided by Texas A&M University
Citation: Research suggests use of neural networks to harness wind and solar energy (2024, May 31) retrieved on 31 May 2024 from https://techxplore.com/information/2024-05-neural-networks-harness -solar-power.html
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