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Vol. 5 No. 1 (2024)
2024 Edition

Articles

AI-Based Analysis and Prediction of Synergistic Development Trends in U.S. Photovoltaic and Energy Storage Systems

Published 2024-08-21

  • Qi Shen
    • ,
  • Xin Wen
    • ,
  • Siwei Xia
    • ,
  • Shuwen Zhou
    • ,
  • Haodong Zhang

Qi Shen
Business Administration, Columbia University, NY, USA

Xin Wen
Applied Data Science, University of Southern California, CA, USA

Siwei Xia
Electrical and Computer Engineering, New York University, NY, USA

Shuwen Zhou
Computer Science, The University of New South Wales, Sydney, Australia

Haodong Zhang
Computer Science, New York University, NY, USA

Abstract

This study investigates the synergistic development trends of photovoltaic (PV) and energy storage systems in the United States, focusing on applying artificial intelligence (AI) for analysis and prediction. The research examines the current state of PV and energy storage deployment, analyzing market trends, technological advancements, and policy landscapes. AI applications in renewable energy generation forecasting, energy storage optimization, intelligent grid management, and predictive maintenance are extensively explored. The study reveals that AI-driven integration of PV and storage systems can increase overall system efficiency by up to 28% compared to traditional approaches. Advanced deep learning techniques, such as recurrent neural networks and extended short-term memory networks, have demonstrated exceptional energy demand and solar generation forecasting capabilities, enabling more accurate predictions and efficient energy management. Implementing AI-based control strategies in grid operations has resulted in a 45% reduction in power outage duration and a 38% decrease in outage frequency. Economic analysis projects that AI-driven optimizations could reduce the levelized cost of electricity for solar-plus-storage projects by up to 25% by 2030. The research concludes that the synergistic development of AI, PV, and energy storage technologies presents a powerful pathway for transforming the U.S. energy landscape, accelerating progress toward a more sustainable, resilient, and efficient energy system. Future research directions and policy implications are discussed further to advance the integration of AI in renewable energy systems.

Keywords

Artificial Intelligence, Photovoltaic Systems, Energy Storage, Renewable Energy Integration

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