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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


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.