learning hvac interim

Timothy M. Dockins – University of Texas at Arlington

research funded by NSF Grant CNS-0649229

Growing concern over high energy cost and usage of fossil fuels

Lack of cohesive plan in the US to manage the use of fossil fuels

Federal regulation at the producer end is not solving the problem

Apply technology at both consumer and producer level

A layman’s control strategy may not be optimal

31%

Design a system that can learn the dynamics of a residential home and it’s occupants and develop an optimal control strategy to accomplish the following objectives simultaneously:

Minimize cost of running HVAC equipment Maximize the comfort of the occupants

taking multiple occupants into consideration

Thermal Comfort: Standards Predictive Mean Vote – Fanger ISO 7730

Thermal Control: General PID, Adaptive Recovery, etc

Thermal Control: Machine Learning Approaches Artificial Neural Networks Fuzzy Systems Evolutionary Algorithms Brain Emotional Learning

Glue: Generalized Predictive Control Machine Learning techniques applied to various points of

prediction▪ NN for occupancy prediction, NNARX for temperature prediction▪ Fuzzy logic system for thermal comfort prediction

u u u u u u u u u u u u

120 minutes

10 minutes

time

horizon

}1|0{ onoffu 4096 permutations

t0

kt

t ttkUuxcue

ku 0

0 1)()(1limminargˆ

Outdoortemperature

sensor

OccupancySensors

ThermalSpaceModel

HomeOccupancyPredictor

Thermal SensationModel

PredictiveOptimalController

environment controller

HVAC

energy cost

comfort cost

AirTemperature

RadiantTemperature

AirVelocity

RelativeHumidity

CClothing

Level

CActivity

Level

Outdoortemperature

sensor

OccupancySensors

ThermalSpaceModel

HomeOccupancyPredictor

Thermal SensationModel

PredictiveOptimalController

environment controller

HVAC

energy cost

com

fort

cost

AirTemperature

RadiantTemperature

AirVelocity

RelativeHumidity

CClothing

Level

CActivity

Level

occupants

Single occupant’s thermal sensation modeled and predicted using a fuzzy system

Combine multiple occupant models using fuzzy logic

Neutral*CoolWarm

21 PP