Big Data Applied to Big Buildings to Give Big Savings on Big Energy Bills
Boris Savkovic, BuildingIQ
Heating, ventilation, and air conditioning (HVAC) systems that regulate internal temperature and humidity in large-scale buildings (office buildings, hospitals, shopping centers, casinos, and so forth) account for approximately 30% of total global energy consumption. HVAC systems are highly inefficient, thereby resulting in unnecessary energy waste. This inefficiency stems from the fact that most HVAC control systems are passive and do not actively and predictively take into account changing weather patterns, weather forecasts, variable energy costs and tariffs, and the underlying building thermal properties in order to optimally control and regulate the building’s internal temperature and humidity so as to minimize total energy consumption.
In collaboration with the Commonwealth Scientific and Industrial Research Organisation (CSIRO), Australia’s national science agency, BuildingIQ has developed the first and only cloud-based software, employing sophisticated big data machine learning methods that continuously optimize HVAC performance in real time for minimum energy consumption, while ensuring maximum comfort for building occupants. The key advantage of this industry-leading software is that it seamlessly interfaces with current building control systems, requiring little to no capital investment for deployment within most existing building control systems. In addition to seamless integration, the software also delivers results for clients, generally achieving energy savings of 10–25% on HVAC operations, depending on the underlying building and HVAC dynamics.
This presentation gives a basic outline of the problem, implementation, energy savings achieved, and challenges in translating R&D into practice, as well as how MATLAB® was employed for basic algorithm development and for interfacing with the rest of the BuildingIQ cloud-based system.
Recorded: 25 Mar 2015
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