Optimizing Energy Consumption in Smart Buildings Through Web-Integrated AI and Cloud-Driven Control Systems
Keywords:
Smart buildings, energy optimization, integrated circuits, energy consumption optimization, cloud-controlled factory, cloud-controlled industry, cloud control, cloud control policy planning, web-integrated, web-integrated artificial intelligence system, web-integrated AI, web-integrated AI system, web-integrated artificial intelligence.Abstract
The rapid development of smart buildings has contributed substantially to the effort of enhancing energy efficiency and reducing greenhouse emissions. Towards this end, the implementation of AI technologies and cloud-driven control programs that analyze and adapt building operations pose a natural next step. Smart buildings should leverage AI for continual energy-consumption-cycle (“ADVISOR”) optimization through demand response, in-depth analytics, fault detection, predictive maintenance, and anomaly mitigation.
The extensive information generated by building-process automation and control (PAC) systems, such as supervisory control and data acquisition (SCADA) data, renders everyday operation and maintenance both complicated and time consuming. These information resources can, however, be exploited to systematically optimize the building’s real-time energy consumption with the help of computer-based analytical artificial intelligence (AI) programs. The combination of a web-integrated AI system that monitors building processes and a cloud-based control-system platform that can dynamically manage operation responses is proposed to address the challenge of optimizing energy consumption in smart buildings.
