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Your Building Has A Story To Tell: Let AI Narrate It
The smarter and more connected buildings become, the more complex the systems behind them must be. Each door opened, light switched on, and temperature adjusted offers a glimpse into the heartbeat of the building, painting a detailed picture of its operational rhythms. While this may sound like a headache and a complicated maze of access and permissions to IT teams, the opportunities unlocked with AI-powered buildings are unmatched. Through automation, buildings can anticipate and predict the future needs of a building, ensuring optimal performance, energy efficiency, and an enhanced user experience. Even though most buildings aren’t even at this automated level of operation, AI can take it a step further and may be the push owners and operators need to invest in smarter tech. In a world increasingly driven by data, understanding and optimizing your building’s operations is no longer a luxury—it’s a necessity.
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Indoor environmental wellness index (IEW-Index): Towards intelligent building systems automation and optimization
The impact of indoor environment quality (IEQ) on occupants’ wellness and productivity is substantial. Simultaneously, due to buildings’ significant role as one of the largest energy consumers, there is a pressing need for improved energy efficiency and intelligence in building design and operation. This paper introduces a novel indoor environmental wellness index (IEW-Index) as a real-time key performance indicator (KPI) derived from controllable factors associated with wellness. The main objective of this index is to develop a universally applicable metric for IEQ, incorporate real-time data and personalized settings, and facilitate the real-time optimization of building control systems. In addition, a novel framework based on a multi-agent system (MAS) for building control systems is introduced, encompassing a detailed design of components and communication protocols. The IEW-Index’s effectiveness has been successfully demonstrated through three real-world case studies formulated using the developed MAS framework. The findings indicate a direction for creating a sustainable building automation system that is intelligent and autonomous.