Building managers and operators often are challenged with the repercussions of unforeseen building systems and equipment failure, such as an interruption to heating and cooling, lighting systems or elevators. In a recent study, 98 percent of respondents claim one hour of electricity or equipment outage cost their business $100,000 on average, posing a significant financial burden.
As more advanced sensors and IoT devices become prevalent within the smart building industry, there is an increase in the amount of information building operators can use to remediate unplanned system failures. IoT devices generate massive amounts of data which are usually sent to the cloud for processing to generate actionable insights. Sending such large volumes of data to the cloud adds latency, poses security risks and decreases building systems efficiency. In all things smart, be it smart cars, smart cities, smart industries, the need to act on real-time information is important for operational effectiveness and to prevent undesirable follow-up events. While traditional IoT computing helps building managers identify the cause of unplanned system failures to enable remedial efficiency, it may not help predict when the problem will occur and proactively prevent such occurrence.
Edge Computing as the Answer
Edge computing empowers building managers by adding real-time precision to analytics so the IoT data can be processed without down-sampling and closer to the source of data production. Rather than relying on delayed insights from the cloud, operators can benefit from real-time insights to identify the cause of system failure, speedup remedial measures and prevent a recurrence.
For instance, edge computing-enabled insights allow operations personnel to monitor energy needs and usage in real time and proactively channel operations, to avoid system overload and related inefficiencies. Building managers traditionally relied on their utility company reports to gain these insights, which may take upwards of six months to receive after an outage occurs and contain gaps in details on usage and outage cause.
Minimizing system and equipment failures is just the tip of the proverbial iceberg in terms of how edge computing can increase a building’s efficiency and increase cost-savings. When you add AI to edge computing, building managers can now leverage that combinatorial power to enable smart, proactive, and preventative maintenance capabilities.
Edge AI Predictive Maintenance
AI on the edge enables a building’s IoT system to monitor operations effectively to deliver deeper insights. Such systems sense data patterns across a multitude of devices, as well as correlate and analyze data in real-time. These insights can proactively alert operations personnel to potential inefficiencies or system failures before they occur.
Edge AI-enabled operational intelligence maximizes system efficiencies, allowing operators to respond to rapidly changing conditions. For example, ability to react to unplanned building closures by sensing human dynamics and abstain from heating, cooling, or lighting those rooms to normal levels, thereby saving energy and money. Additionally, provisioning in-building climate comfort based on real-time room dynamics, occupancy, and external weather factors while catering to energy efficiencies and equipment considerations.
Prescriptive maintenance can be used to reduce expensive repair and maintenance costs while minimizing system downtime and extending life of mechanical systems. Furthermore, energy use optimization helps operators balance reliability, performance, and cost, in addition to automating the task of monitoring the building’s efficiencies in real-time.
Going Deeper with the Benefits of Edge AI
Edge AI helps managers and operators accomplish more than just predicting maintenance needs and reducing unplanned system and equipment failures, including:
Edge AI enables building supervisors to assemble a complete and continuously improving picture of their buildings. Ensuring minimal system and equipment disruptions improves operational efficiency and helps take proactive steps to improve occupant comfort.
Predictive Maintenance and So Much More
Being able to act and react to events of interest as (or before) they occur is the key to situational intelligence and operational effectiveness. Fueled by machine learning and located right at the heart of the IoT network, edge AI brings us one step closer to a world where unforeseen building system and equipment failures do not adversely impact productivity levels or overall business. By leveraging advancements in edge AI technologies, organizations can improve their efficacies to offer occupants a safe and comfortable building habitat, while at the same time improving cost savings.
About the author: Senthil Kumar is VP of Software Engineering at FogHorn. He is a technology executive with leadership experience in building scalable analytic platforms, enterprise software, and distributed computing architectures across North America, Europe, and Asia. He has helped bring to market products and technologies in A.I, Cloud computing, IoT, Big Data Analytics, Decision Science, Block Chain, and Visual Analytics, within the fields of Enterprise Decision Management, Catastrophic Risk Management, Network infused applications, A.I and Healthcare.
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