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The Engineering East building, which houses
the FAU College of Engineering and Computer
Science and the NSF I/UCRC, is LEED Platinum
certified and relies on the newest green
technologies to reduce its energy usage and
environmental footprint. The building power,
HVAC systems, and the server room are heavily
instrumented with hundreds of sensors. For this
project, we developed predictive models for
energy systems and room comfort. These models
are used in simulations to optimize building
operations. An earlier study looked at the
efficiency of the solar power system, currently
generating 7-13% of the total consumed power.
We investigate the relationships between the
outside environmental parameters and the
power generated. A model for power generation
is designed to be used in the later phases of
the project involving simulations. Early results
indicate a strong correlation (84%) between
the sky light level and generated power. We
measured a loss in efficiency in the early
afternoon explained by the panels being in the
building’s shadow in the late afternoon, shown
in the chart.
In this project, we also use data mining
techniques to identify the relationships between
room comfort level (defined by temperature,
CO2, and humidity), HVAC parameters (air
inflow temperature, room volume, occupancy),
and external parameters (sun exposure, outside
Smart Building Optimization Systems and Algorithms
Ionut Cardei and Borko Furht, PIs
l
Student: Luis Bradley
p ro j e ct 7
4 0
temperature, light, barometric pressure,
precipitation). We derive the most relevant
parameters for predicting room comfort, and
associations between a desired comfort level
and controllable or environmental metrics.