Office buildings, hospitals, and other large-scale commercial buildings account for about 30% of the energy consumed worldwide. The heating, ventilation, and air-conditioning (HVAC) systems in these buildings are often inefficient because they do not take into account changing weather patterns, variable energy costs, or the building’s thermal properties.
BuildingIQ has developed Predictive Energy Optimization™ (PEO), a cloud-based software platform that reduces HVAC energy consumption by 10–25% during normal operation. PEO was developed in cooperation with the Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australia’s national science agency. Its advanced algorithms and machine learning methods, implemented in MATLAB®, continuously optimize HVAC performance based on near-term weather forecasts and energy cost signals.
“CSIRO used MATLAB to develop the initial technology. We continue to use MATLAB because it is the best tool available for prototyping algorithms and performing advanced mathematical calculations,” says Borislav Savkovic, lead data scientist at BuildingIQ. “MATLAB enabled us to transition our prototype algorithms directly into production-level algorithms that deal reliably with real-world noise and uncertainty.”