Artificial Intelligence Applications in Solar Photovoltaic Renewable Energy Systems
Ifeanyi Michael Smarte Anekwe, Emmanuel Kweinor Tetteh, Edward Kwaku Armah
With the increasing reduction of fossil fuel supplies, it is predicted that the world would run out of energy resources within the next few decades attributed to the depletion of fossil fuels. Renewable energy sources generate electricity with minimal contribution of CO2 or other greenhouse gases to the atmosphere. Solar, wind, hydroelectricity, and biomass are the most common kinds of sustainable energy sources, and all of them have enormous prospects to help the world to meet its future energy demand. The solar photovoltaic technique is among the first of several renewable energy systems that have been implemented around the world to meet the basic requirement of electricity, especially in remote regions. The deployment of Artificial Intelligence in the energy sector is becoming more prevalent to ensure an effective energy supply. This chapter presents a review of the application of artificial intelligence in a solar PV system while highlighting the challenges and prospects for effective utilization in the renewable energy system.
Artificial Intelligence, Deep Learning, Internet of Things, Machine Learning, Renewable Energy, Solar Energy, Solar Photovoltaic
Published online , 41 pages
Citation: Ifeanyi Michael Smarte Anekwe, Emmanuel Kweinor Tetteh, Edward Kwaku Armah, Artificial Intelligence Applications in Solar Photovoltaic Renewable Energy Systems, Materials Research Foundations, Vol. 147, pp 47-86, 2023
Part of the book on Application of Artificial Intelligence in New Materials Discovery
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