Fifth & Fifth – Energy Savings

GWLRA is constantly looking to use innovation to improve the performance of its managed assets and reduce their environmental footprint. This past year, our Business Excellence & Innovation (BEI) team and property management team at Fifth & Fifth, a multi-tenant office building in Calgary, selected a novel digital buildings technology vendor to pilot onsite.

The vendor, BrainBox AI, was chosen due to its unique ability to use machine learning to reduce energy consumption, extend equipment life and maintain tenant comfort. The technology works by using historical operational and weather data to implement predictive controls. BrainBox uses these predictions to determine future optimization strategies and then automatically updates the building's HVAC system to minimize equipment run time while still maintaining the targeted space conditions (e.g., air temperature), resulting in energy savings. This autonomous control also allows for building operators to focus on other high priority tasks that wouldn’t be possible with other HVAC optimization solutions that require manual intervention.

BrainBox was piloted on 17 of 35 floors at Fifth & Fifth. The pilot consisted of a setup and learning phase, as well as a measurement and verification phase, to verify predicted results. Two algorithms were deployed: one optimized start/stop of HVAC equipment and the other optimized static pressure (fan speed) of the main highrise air handling units (AHU). Both algorithms targeted reductions of electricity and natural gas consumption in the building.

Preliminary results demonstrated successful implementation of the technology with nearly 100,000 kWh of energy savings, which resulted in 50 tonnes of carbon dioxide equivalent reduced and $50,000 in cost savings for tenants. The team decided to extend the pilot with BrainBox at Fifth & Fifth to realize more savings throughout the building in future years.

Projects like this demonstrate how GWLRA staff embrace innovation to help reduce its energy consumption, utility costs and carbon emissions. A special thanks to Dhebi Hay, Bill Wong, Barry Marcellus, Jonathan Strome and Laura Newcombe for their work to help realize these savings.