The UK is the third biggest energy user in Europe, responsible for 22% of power consumption. Nearly half of this goes into providing comfortable environments in the buildings we occupy. As a vital area for improving energy efficiency it’s by far the biggest target we have.
Over the last 20 years, a combination of the global energy crisis and escalating prices have helped move energy disciplines to a much more prominent role in building design and management. At the same time, industry has begun to develop better tools to help energy consultants identify areas for energy savings. All energy management strategies focus on reducing energy use without compromising the comfort of occupiers. Now CBRE has developed a specific tool that gives landlords and occupiers clear, real time visibility of energy use and the ability to control it effectively.
Typically, energy demand in complex buildings is managed digitally by a Building Management Systems (BMS). A BMS can interpret demand and allocate the appropriate resources to satisfy it in the right amount and at the right time. The data collected by a sensor network is translated through predefined algorithms issuing instructions to plant and equipment throughout the building to deliver the desired comfort conditions.
These algorithms are sets of rules hard-coded in the BMS logic unit designed to analyse information and perform corresponding functions. This can range from very simple instructions like turning on a heater if a room is cold or complicated building wide strategies to optimise all energy usage. For example, the system might be programmed to take advantage of free cooling or manage the routine used to warm up or cool down the building at either end of the day. Analysing the information from the sensors highlights anomalies, allows energy usage to be fine-tuned and improves the comfort of occupiers.
A standard BMS can’t store all the information coming from the sensors, nor can it cross check data from different sources across the building. A new layer of technology is needed to unlock the full potential of this data. It would sit above the BMS architecture to observe the operation of the building, establishing links between different data and identify patterns.
We have turned this idea into a tool, developed by CBRE’s London Engineering team. Based on the idea of the Internet of Things and Big Data, CBRE’s proprietary technology can retrieve real time information from a building’s BMS, analyse it to yield valuable insights about building operation and, ultimately develop effective strategies to reduce energy use. In addition, the tool supports a condition-based maintenance regime as well as real time indoor air quality monitoring to enhance occupier wellbeing.
Known as Asset IQ, the tool is a web-based platform designed to connect remotely to a BMS and capture live data from the building. It can automate building maintenance, allowing users to track power, lighting, ventilation and air-conditioning systems by collecting data such as operating hours, idle times, and maintenance cycles.
At the same time, we have developed an enhanced energy offer which uses this platform to gain a deep understanding of buildings, so we can provide support to building management teams, pursuing the best possible energy performance in their buildings.
Using Asset IQ across 3.5 million ft2 of commercial buildings managed by CBRE Asset Services we delivered a 10% reduction in energy use and implemented more than £1.2 million of energy conservation measures. The return on investment from using Asset IQ for this exercise was a staggering 234%.
Energy management everywhere is embracing technology to deliver better energy efficiency. The IoT and the Big Data discipline both offer a significant opportunity for landlords and occupiers to reduce energy expenditure without sacrificing comfort.
Applying Asset IQ results to 10% of the global UK HVAC consumption, would yield a huge reduction of 116,300,000 kWh - all of which can be achieved by simply enhancing the performance of existing infrastructure.
Our next step is introducing artificial intelligence and machine learning into the Asset IQ technology in order to develop an adaptive and real-time algorithm. That offers the promise of greater control, more intelligent energy management decisions and even better savings.