Before the arrival and dissemination of the Internet of Things (IoT) and other smart technologies within the built environment, building systems that controlled climate, lighting, energy, and other aspects affecting operations were siloed. These various systems used disparate protocols that didn’t allow them to communicate with each other. There was little regard for ensuring that they spoke the same “language,” as there was no need for them to do so.
Implementing fault detection in your building can have many benefits. For example, more than 30% of the energy consumed by commercial buildings in the US is derived from HVAC systems. A large portion of that energy is wasted due to a lack of efficiency. The identification of faults within HVAC systems can reduce energy consumption by 5-15%. That’s a huge amount of savings just from optimizing one component of your building.
However, many buildings have very rigid, rule-based fault detection systems in place. This is problematic because a simple system such as this is only able to identify issues within specific sets of conditions. This places limitations on what types of issues can be identified and the extent to which maintenance can be performed.
The utilization of an intelligent fault detection system can significantly impact not only your energy consumption but also your ROI by identifying anomalies and challenges that affect the health and lifetime of individual components and entire systems. By ensuring that your fault detection and diagnostics are backed by an intelligent solution, you’ll be sure to unlock the full potential of your savings, maintenance, and occupant comfort.
Data models visually represent the data gathered throughout a connected IT system, either in whole or in part to facilitate its storage with easy access from a database. In built environments, the importance of data modeling comes down to the ability of elements within this system to communicate. To better communicate, common definitions should show from where the data comes and what it represents. To ensure understanding and lessen confusion, rules are used to describe elements within the system.
In 2020, the New York State Energy Research and Development Authority (NYSERDA) found that integrating smart technologies and real-time energy management solutions can reduce energy costs by around 15% while boosting ROI and increasing employee productivity. With results like these, it’s no wonder smart buildings are considered the future of the commercial real estate. But smart building energy management system benefits go beyond cost savings.
It’s no secret that commercial buildings have high energy demands. Without energy optimization, this demand puts enormous strain on local power grids, has a significant environmental impact, and eats into profits. That’s why forward-thinking building operators are turning to unified IoT for energy management.
As more buildings become smart, there is a growing need for standardized semantic data models that allows for better data use. The goal of using common data models is to enable a better understanding of a smart building’s data in a continuously changing environment. A robust data ontology framework makes this possible and allows your building management system to become truly intelligent.
Smart buildings offer tremendous benefits. From energy reduction to increased productivity to better occupant experiences, it’s no wonder smart technology is becoming an essential part of modern buildings. But with increased connectivity and each device serving as a potential point of entry, smart buildings are vulnerable to cyber attacks in ways that traditional buildings are not.
There is no single set of cybersecurity standards for the design and installation of building control and automation systems. Instead, cybersecurity strategies have historically varied depending on how developers, designers, and vendors approached each building’s requirements. But the network-connected IoT devices used in smart buildings are highly susceptible to cyberattacks. According to a 2020 report:
Data infrastructure can be thought of as a network of roads along which data travels. Poor infrastructure is like trying to drive on roads with missing street signs, toll booths, traffic lights that don’t work, dilapidated bridges, and random roadblocks. These obstacles can make traffic grind to a halt, or even get lost on the way to its destination.
One of the primary goals of integration in smart buildings is providing a unified outlook that facilitates robust historical data analysis. Integration architecture must be designed to support the analysis of data flowing from multiple systems, and this data must be unadulterated, wide-ranging, obedient to a set of rules, reliable, and up-to-date. This requires standardization when the architecture is first designed, and again once when deployed.