Smart building energy management and monitoring system based on artificial intelligence in smart city. (March 2023)

• Record Type: Journal Article
• Authors: Selvaraj, Rajalakshmi; Kuthadi, Venu Madhav; Baskar, S.
• Is Part Of: Sustainable energy technologies and assessments. Volume 56(2023)
• Publication Date: 2023-03
• Subjects: Artificial intelligence -- Energy -- Monitoring system -- Smart buildings -- Sustainability; Renewable energy sources -- Periodicals; Energy development -- Technological innovations -- Periodicals; Electric power production -- Periodicals; Energy storage -- Periodicals; 333.79
• DOI: 10.1016/j.seta.2023.103090
• Languages: English
• ISSNs: 2213-1388

Abstract

Highlights: Artificial Intelligence Technique for Monitoring Systems in Smart Buildings. Implementing efficient strategy for harnessing renewable energy and safety process. Results show the AIMS-SB enhances accuracy and efficiency than other methods. Abstract: In the present scenario, the fastest-growing environmental concerns are energy management and monitoring. In-efficient energy recycling, energy consumption, energy utilization, and drain characteristic are smart building energy management challenges. Hence to examine the connection between smart city management policies and energy management, this research proposed an Artificial Intelligence Technique for Monitoring Systems in Smart Buildings (AIMS-SB) to manage energy consumption and produce and recycle energy required for a smart building. AIMS-SB helps to predict energy analysis, renewable energy production, and recycling evaluation based on prediction model strategies. AIMS-SB developed eco-design monitoring systems for smart buildings to optimize energy consumption, utilization, and drain characteristics. These efficient implementation strategies and methods for harnessing renewable energy help to improve the safety process, recycling, and reuse of our energy resources for smart building energy management. AIMS-SB provides viable solutions to the growing number of challenges associated with smart city energy management. Therefore, the system's findings demonstrate increased accuracy and efficiency compared to conventional methods.