MathWorks today announced that BuildingIQ is using data analytics capabilities in MATLAB to speed up the development and deployment of proactive, predictive algorithms for HVAC energy optimization. BuildingIQ engineers have developed Predictive Energy Optimization™ (PEO), a cloud-based software platform that reduces HVAC energy consumption in large-scale buildings by 10%-25% during normal operation.
BuildingIQ needed to develop PEO as a real-time system that would help minimize HVAC energy costs in large-scale commercial buildings via proactive, predictive optimization. The team used MATLAB algorithms integrated in a production cloud environment to optimize occupant comfort while minimizing energy costs. BuildingIQ engineers used Signal Processing Toolbox to filter data, Statistics and Machine Learning Toolbox for algorithms to model contributions of gas, electric, and solar power to heating and cooling processes, and Optimization Toolbox to continuously optimize energy efficiency in real time. To integrate the resulting algorithms into the production systems the team used MATLAB Compiler for deployment, saving time and resources from translating MATLAB algorithms into Java or C.
“We use MATLAB because it is the best tool available for prototyping algorithms and performing advanced mathematical calculations,” said 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.”
“While companies look for more intelligence from their data, they often lack the resources and expertise in analyzing and visualizing gigabytes of data, quickly developing algorithms, and finding the best suited algorithmic approach,” said Paul Pilotte, technical marketing manager, MathWorks. “BuildingIQ is setting a benchmark with its ability to analyze and visualize big data sets, deploy these advanced optimization algorithms, and run them in a production cloud environment.”