Data Analytics and Artificial Intelligence in Smart Buildings
Smart Buildings are generally monitored and controlled by traditional Building Management System (BMS). With the tremendous growth of sensors and Internet of Things (IoT) devices installed in the past few years, most buildings nowadays are equipped to become a smart building.
The following are some of the key use-cases of data in a smart building:
Reduce manpower usage in preventive maintenance by adopting a data-driven approach
Improve energy efficiencies with data-driven HVAC AI control
Enhance tenants satisfaction with better environment control and feedback
The data generated from these sensors have not been fully utilised now. This is mainly due to:
the process to extract the historical trend log is time-consuming and costly through traditional BMS;
the data insights generated are often non-actionable;
there is a lack of ROI in data project.
With a centralised building analytics platform, the above pain points can be addressed, presenting a huge opportunity to make building operation and maintenance smarter through data.
The major benefit of a centralised data platform is that real-time self-service data access has become possible. The traditional workflow of acquiring data would require maintainers downloading trend logs from controllers. This would typically cause hours of delay, and any analysis performed on this workflow would be outdated right after it is completed.
The real-time access of data and corresponding real-time analytics would also allow site operators to spot and diagnose issues as soon as symptoms start appearing. These real-time alerts and controls would generate improvement in building operations and thus reduce human resources and energy costs.
In the past, maintenance practices are mostly based on scheduled maintenance and experience. Analytics capabilities are limited to alerts and alarm monitoring. With a smart building analytics platform, site operators can reduce manpower usage with just-in-time data-driven predictive maintenance. This replaces the human-intensive periodic maintenance approach, reducing the number of emergency calls by up to 80% as faults are often spotted early and fixed as soon as possible.
AI data-driven HVAC controls
Current HVAC control logic is often programmed without much consideration of energy usage. With the help of a data-driven platform, we can improve the HVAC system's energy efficiency and accomplish over 20% recurring weather normalised energy savings for equipment in a sizable commercial building.
Environment control and monitoring
Furthermore, the data analytics platform can enhance tenants satisfaction with better real-time environment control and feedback. There is currently smart thermostat that allows the dynamic control of indoor environment conditions according to the occupancy rate and the external weather conditions.
How to introduce Smart Analytics in Buildings
There is often substantial resistance when introducing advanced analytics to a current building operation:
Operation stability is the highest priority for building operation team - this has often led to a very conservative approach to new technology, especially in system control. The development of fault tolerance AI system would alleviate this concern.
Integration sometimes hindered by proprietary protocol - sometimes data is locked behind the proprietary protocol, this will become lesser of an issue in the future as newly commissioned systems are often designed with open protocol and easy access of data.
ROI has been questionable in the past. With the latest iteration of SaaS-based AI smart controls system, energy-saving realised is much higher than the subscription cost of such software, accumulating instant saving over time.
With the maturation of the data ecosystem in smart building, we believe the adoption of smart building analytics and AI-based control system is likely to become more common.
More about the author: Chris is the co-founder of Carnot innovations, a company applying a data driven approach to building management with advanced machine learning fault detection and AI controls. If you are interested in learning more about Carnot’s solutions, visit here.
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