1 College of Architecture Construction and Planning, Department of Architecture, The University of Texas at San Antonio, Texas, USA.
2 Scott Sutherland School of Architecture & Built Environment, Robert Gordon University, United Kingdom.
3 Department of Sustainability Technology and Built Environment (Concentration in Renewable Energy Technology), Appalachian State University, Boone, North Carolina, USA.
4 University of Houston Downtown, USA.
Review Article
World Journal of Advanced Research and Reviews, 2024, 23(02), 1333-1348
Received on 05 July 2024; revised on 13 August 2024; accepted on 16 August 2024
The integration of Artificial Intelligence (AI) in the renewable energy sector has revolutionized the efficiency and utilization of renewable energy sources in contemporary construction, significantly impacting visual power plant operations. AI technologies, including machine learning and predictive analytics, optimize energy production, enhance system performance, and streamline maintenance processes, thereby driving the adoption and efficiency of renewable energy systems. AI-powered predictive maintenance tools analyze vast amounts of data from renewable energy installations, such as solar panels and wind turbines, to predict and prevent equipment failures. This proactive approach reduces downtime and maintenance costs, ensuring continuous and efficient energy generation. Additionally, AI algorithms optimize energy storage and distribution by accurately forecasting energy production and demand. This optimization helps balance supply and demand, reducing reliance on non-renewable energy sources and enhancing grid stability. In visual power plants, AI enhances operational efficiency through advanced monitoring and control systems. Real-time data from sensors and IoT devices are processed by AI to provide actionable insights, enabling operators to make informed decisions promptly. AI-driven automation in visual power plants ensures optimal performance by adjusting parameters in real-time, based on environmental conditions and energy demand, thus maximizing energy output. Moreover, AI facilitates the design and construction of smart buildings and infrastructure, integrating renewable energy systems seamlessly. AI-driven energy management systems in buildings analyze consumption patterns, optimize energy use, and promote energy-saving practices, contributing to overall energy efficiency. This integration not only reduces the carbon footprint of construction projects but also aligns with global sustainability goals. The impact of AI on boosting renewable energy utilization and visual power plant efficiency is profound. By leveraging AI technologies, contemporary construction can achieve higher energy efficiency, lower operational costs, and increased sustainability. As AI continues to evolve, its role in the renewable energy sector will likely expand, further enhancing the capabilities of renewable energy systems and paving the way for a more sustainable future in construction and beyond. This paper underscores the transformative potential of AI in renewable energy and its critical role in shaping the future of sustainable construction practices.
Impact; AI; Renewable Energy; Power Plant Efficiency; Contemporary construction
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Helena Nbéu Nkula Manuel, Husseini Musa Kehinde, Chijioke Paul Agupugo and Adélia Cestina Nkula Manuel. The impact of AI on boosting renewable energy utilization and visual power plant efficiency in contemporary construction. World Journal of Advanced Research and Reviews, 2024, 23(2), 1333-1348. Article DOI: https://doi.org/10.30574/wjarr.2024.23.2.2450