BuildingIQ Develops Proactive Algorithms for HVAC Energy Optimization in Large-Scale Buildings

BuildingIQ needed to develop algorithms that could continuously process gigabytes of information from a variety of sources, including power meters, thermometers, and HVAC pressure sensors, as well as weather and energy cost data. A single building often produces billions of data points, and the scientists and engineers needed tools for efficiently filtering, processing, and visualizing this data.

To run their optimization algorithms, the scientists and engineers had to create an accurate mathematical model of a building’s thermal and power dynamics. The algorithms would use this calculated model to run constrained optimizations that maintained occupant comfort while minimizing energy costs.

BuildingIQ needed a way to rapidly develop mathematical models, test optimization and machine learning approaches, prototype algorithms, and deploy them into its production IT environment.

BuildingIQ used MATLAB to speed up the development and deployment of its predictive energy optimization algorithms.

The optimization workflow begins in MATLAB, where BuildingIQ engineers import and visualize 3 to 12 months of temperature, pressure, and power data comprising billions of data points. They use Statistics and Machine Learning Toolbox™ to detect spikes and gaps, and remove noise produced by sensor failures and other sources using filtering functions in Signal Processing Toolbox™. BuildingIQ engineers fitted a mathematical model developed in MATLAB to the denoised data using least squares fitting functions from Optimization Toolbox™. This measurement and verification (M&V) model correlates ambient temperature and humidity with power consumed by the HVAC system.

As part of the modeling process, they use SVM regression, Gaussian mixture models, and k-means clustering machine learning algorithms from Statistics and Machine Learning Toolbox to segment the data and determine the relative contributions of gas, electric, steam, and solar power to heating and cooling processes.

The team builds a PEO model in MATLAB that captures the effect of the HVAC system and ambient conditions on internal temperatures in each zone, as well as on the total power consumption for the building. Using Control System Toolbox™ they analyze HVAC control system poles and zeros to estimate overall power consumption and determine how quickly each zone is likely to converge to its set point.

BuildingIQ engineers use Optimization Toolbox and the PEO model to run multi-objective optimizations with hundreds of parameters, as well as nonlinear cost functions and constraints to continuously optimize energy efficiency in real time. These optimizations take into account projected weather and energy prices over the next 12 hours, and identify optimal HVAC set points. In operation, Java^® software in the cloud invokes the MATLAB optimization algorithms periodically throughout the day.

Each day, BuildingIQ calculates baseline energy cost from the M&V model representing what the client would have paid for HVAC energy without the BuildingIQ PEO platform. Savings range from 10% to 25%.