Glasgow-based building energy performance company, arbnco, has launched a new smart building tool that is able to benchmark, track and forecast energy consumption, providing a level of data analysis and building control previously unattainable for the UK commercial property market.
The software, arbn insight, will enable building managers and owners to compare their building’s energy performance against its peers, predict future performance based on climate predictions, and offer behavioural and practical recommendations for improvements.
Designed to transform building data into actionable insights, the software can be used to analyse the performance of a single building, or deployed across an entire property portfolio.
After the raw data file for a building - containing size, age and energy consumption - has been uploaded to the platform, and data on occupancy hours and building function has been inputted, energy managers are presented with a forensic analysis of operational energy performance, which can be broken down into months, weeks, or days.
The software’s benchmarking function allows energy managers to easily compare the building’s performance against its peer buildings; for example, whether it is consuming more energy for lighting than a typical building of its size, function and occupancy levels should require.
It is given an EnergyStar rating, an internationally recognised independent benchmark for measuring energy efficiency, along with specific metrics designed by arbnco, including a Daily Mean Load score and a Base Load Factor. Each of these metrics are rated out of one hundred and measured against the median for similar buildings. Individual days can be compared to the benchmark, providing energy managers with the ability to hone in on what particular behaviours or patterns might be contributing to a higher than average consumption.
The software presents all data in easy-to-understand visual forms, such as graphs and pie charts. It breaks down energy use into hours occupied and unoccupied, and can disaggregate the data into consumption by type, such as lighting and heating.
After an energy wastage figure is identified, arbn insight makes improvement recommendations - both behavioural changes (such as switching off lights in meeting rooms), and technical changes - with estimates for subsequent energy savings made. Analysis is also carried out on the relationship between energy use and temperature, delivering an understanding of energy performance independent of the weather.
Using machine learning, the software is also able to generate a six-month forecast of future energy usage, based on predicted climate trends and the existing building data provided. This is retained and compared with actual building data as it is generated. Any diversion from the projections, if not easily explainable, can be quickly and reliably analysed and addressed.
arbn insight also enables energy managers to measure the impact of energy efficiency measures post-implementation. The software will log improvement activity and track the resulting energy savings made.
arbnco is working to incorporate arbn insight with its indoor environment monitoring technology, correlating each building’s energy use in real time with its internal environment, adding an extra dimension to the software’s analysis.
Simon West, co-founder and chief operating officer of arbnco, commented: “arbn insight has the potential to be something really transformative for building and facilities managers. The push towards net-zero in real estate continues to ramp up, but trying to achieve net-zero without forensic knowledge of operational energy use will always be an uphill battle. We believe that arbn insight will answer this need, arming managers with the information they need to make informed and intelligent decisions to improve their energy efficiency.
“We hope arbn insight can also help to educate managers on possible improvement measures they could make, recommending both technical and behavioural solutions to reduce wastage, and providing them with an empirical representation of the return on investment for implementation.”