To advance energy efficiency in buildings, the center focuses on research efforts centered around developing AI-driven and data-driven tools. Key research ideas include:
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AI-Powered Energy Management Systems: Developing advanced AI algorithms to monitor and optimize building energy consumption. These systems would analyze real-time and historical data to predict energy demand, identify inefficiencies, and suggest adjustments to HVAC, lighting, and other systems for optimal performance.
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Data-Driven Energy Efficiency Analytics: Creating tools that leverage big data and machine learning to provide actionable insights into building energy usage patterns. These tools could identify trends, forecast energy needs, recommend targeted efficiency improvements, and facilitate predictive maintenance for critical systems.
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Digital Twin Technology for Building Performance: Designing digital twin models of buildings that use AI and real-time data to simulate energy performance.
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Analytical Tools for Energy Monitoring: Developing advanced tools for energy monitoring, such as bill analysis software, that integrates with utility data to track energy consumption and costs over time. These tools would provide insights into peak usage patterns, detect anomalies, and suggest actionable changes to reduce energy consumptions.
