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A real-time model predictive control for building heating and cooling systems based on the occupancy behavior pattern detection and local weather forecasting

  • Research Article
  • Architecture and Human Behavior
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Abstract

Current research studies show that building heating, cooling and ventilation energy consumption account for nearly 40% of the total building energy use in the U.S. The potential for saving energy through building control systems varies from 5% to 20% based on recent market surveys. This papers introduces and illustrates a methodology for integrated building heating and cooling control to reduce energy consumption and maintain indoor temperature set-point, based on the prediction of occupant behavior patterns and local weather conditions. Advanced machine learning methods including Adaptive Gaussian Process, Hidden Markov Model, Episode Discovery and Semi-Markov Model are modified and implemented into this study. A Nonlinear Model Predictive Control (NMPC) is designed and implemented in real-time based on dynamic programming. The experiment test-bed is setup in the Solar House, with over 100 sensor points measuring indoor environmental parameters, power consumption and ambient conditions. The experiments are carried out for two continuous months in the heating season and for a week in the cooling season. The results show that there is a 30.1% measured energy reduction in the heating season compared with the conventional scheduled temperature set-points, and 17.8% energy reduction in the cooling season.

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Abbreviations

A :

wall or window surface area (m2)

A f :

floor area (m2)

C p_air :

specific heat of the air (J/(kg·°C))

C out :

volumetric heat capacitance of the external part of the wall (J/(m3·°C))

C int :

volumetric heat capacitance of the internal part of the wall (J/(m3·°C))

C MR_wall_in :

volumetric heat capacitance of the internal part of the wall in meeting room (J/(m3·°C))

C oz_wall_in :

volumetric heat capacitance of the internal part of the wall in other zone (J/(m3·°C))

C Off_wall_in :

volumetric heat capacitance of the internal part of the wall in office (J/(m3·°C))

C p_MR_cf :

specific heat of concrete floor (J/(kg·°C))

C r :

correction factor due to the impact of indoor furniture

D :

characteristic length (m)

h cf :

overall heat transfer coefficient for floor surface, which includes both radiation and convection (W/(m2·°C))

m cf :

concrete floor mass (kg)

\(\dot m_{fcu}\) :

air mass flow rate for fan coil unit (kg/s)

l :

length (m)

Q surfo :

outside surface heat flux (W)

Q sol_out :

solar radiation on the outside surface of the wall (W)

Q :

internal radiative heat gains absorbed by inside surface of the wall (W)

Q FCU_MR :

load from fan coil unit cooling (W)

Q int :

load from internal heat gain (W)

Q inf :

load from internal infiltration (W)

h cf_cov_n :

natural convection (W/(m2·°C))

h cf_cov_f :

forced convection (W/(m2·°C))

R wall :

thermal resistance of wall (°C/W)

R MR_air :

thermal resistance of indoor air (°C/W)

R MR_co_f :

thermal resistance of combined effects of radiation and convection between room air and radiant heating floor (°C/W)

R MR_cf :

thermal resistance of concrete floor in meeting room (°C/W)

R oz_MR :

thermal resistance between meeting room and other zone (°C/W)

R MR_Off :

thermal resistance between meeting room and office (°C/W)

k :

thermal conductivity of surface (W/(m·°C))

ΔT :

indoor-outdoor temperature difference (°C)

T amb :

ambient air temperature (°C)

T isurf :

inside surface temperature (°C)

T osurf :

outside surface temperature (°C)

T isur_MR_in :

surface temperature of the i-th internal wall (°C)

T MR_in :

meeting room air temperature (°C)

T Off_in :

office room air temperature (°C)

\(\bar T_{MR\_w}\) :

temperature around the water tubes (°C)

U wf :

water to floor heat transfer coefficient (W/(m2·°C))

X i :

i-th state in Markov Model

X :

a vector of unknown parameters

X L :

lower bound of unknown parameters

X U :

upper bound of unknown parameters

Nu :

Nusselt number

Pr :

Prandtl number

Re :

Reynolds number

α :

coefficient of absorbed solar radiation on the external surface of an external wall

α hp :

correction factor for heat pump energy consumption

β :

coefficient of absorbed transmitted solar radiation on the inside surface of an external wall

γ :

coefficient of absorbed internal heat gains from occupancy and equipments by inside surface of the wall

σ :

Stefan-Boltzmann constant (W/(m2·K4))

φ(·):

cost function of NMPC

g(·):

heat transfer functions

ε :

surface total Hemispherical emissivity

λ :

fluid conductivity (S/m)

cf:

concrete floor

f:

floor

g:

ground

in:

indoor

MR:

meeting room

Off:

office room

out:

outdoor

oz:

other zone

rf:

roof

sur:

surface

wi:

water inlet

wo:

water outlet

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Authors and Affiliations

  1. Department of Mechanical Engineering, University of Texas at San Antonio, San Antonio, TX, 78294, USA

    Bing Dong

  2. School of Architecture, Carnegie Mellon University, Pittsburgh, PA, 15213, USA

    Khee Poh Lam

Authors
  1. Bing Dong
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  2. Khee Poh Lam
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Correspondence to Bing Dong.

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Dong, B., Lam, K.P. A real-time model predictive control for building heating and cooling systems based on the occupancy behavior pattern detection and local weather forecasting. Build. Simul. 7, 89–106 (2014). https://doi.org/10.1007/s12273-013-0142-7

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  • DOI: https://doi.org/10.1007/s12273-013-0142-7

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