Volume- 12
Issue- 5
Year- 2024
DOI: 10.55524/ijircst.2024.12.5.6 | DOI URL: https://doi.org/10.55524/ijircst.2024.12.5.6
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This is an Open Access article distributed under the terms of the Creative Commons Attribution License (CC BY 4.0) (http://creativecommons.org/licenses/by/4.0)
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Qi Shen , Xin Wen, Siwei Xia, Shuwen Zhou, Haodong Zhang
This study examines the convergence of the development of photovoltaic (PV) and energy storage in the United States, focusing on using artificial intelligence (AI) for analysis and forecasting. Research examines the current state of PV and energy deployment and reviews the industry, technological advancements, and policy areas. AI applications for forecasting, energy storage optimization, intelligent grid management, and predictive maintenance are widely explored in renewable energy generation. This study shows that AI-driven integration of PV and storage systems can increase the overall efficiency by up to 28% compared to traditional methods. Deep learning techniques, such as neural networks and short-term continuous networks, have demonstrated the uniqueness of energy demand and solar energy forecasting capabilities, enabling more predictability and efficient energy management. Implementing AI-based control strategies in grid operations has resulted in a 45% reduction in power outage time and a 38% reduction in power outage frequency. Business studies show that AI-engineered optimization can reduce energy costs for solar-plus-storage projects by up to 25% by 2030. Research conclusions that are integrating AI, PV, and electronics have revealed a powerful way. For changing the US energy landscape, making progress toward a more efficient, robust, and sustainable energy system. Future research directions and policy implications are further discussed to support the integration of AI in renewable energy systems.
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Master of Business Administration, Columbia University, NY, USA
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