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RESEARCH PAPERS

Sensitivity Analysis of Optimal Building Thermal Mass Control

[+] Author and Article Information
Gregor P. Henze, Anthony R. Florita

Architectural Engineering, University of Nebraska-Lincoln, Omaha, NE 68182

Thoi H. Le

 Fraunhofer Institute for Solar Energy Systems, D-79100 Freiburg, Germany

Clemens Felsmann

Institute for Thermodynamics and Building Systems Engineering, Technical University of Dresden, D-01065 Dresden, Germany

J. Sol. Energy Eng 129(4), 473-485 (May 19, 2006) (13 pages) doi:10.1115/1.2770755 History: Received May 22, 2005; Revised May 19, 2006

In order to avoid high utility demand charges from cooling during the summer and to level a building’s electrical demand profile, precooling of the building’s massive structure can be applied to shift cooling-related thermal loads in response to utility pricing signals. Several previous simulation and experimental studies have shown that proper precooling can attain considerable reduction of operating cost in buildings. This paper systematically evaluates the merits of the passive building thermal capacitance to minimize energy cost for a design day using optimal control. The evaluation is conducted by means of a sensitivity analysis utilizing a dynamic building energy simulation program coupled to a popular technical computing environment. The optimal controller predicts the required extent of precooling (zone temperature set-point depression), depending on the utility rate structure, occupancy and on-peak period duration and onset, internal gains, building mass, occupancy period temperature set-point range, and weather as characterized by diurnal temperature and relative humidity swings. In addition to quantifying the building response, energy consumption, and utility cost, this paper extracts the dominant features of the optimal precooling strategies for each of the investigated cases so that guidelines for near-optimal building thermal mass savings may be developed in the future.

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Copyright © 2007 by American Society of Mechanical Engineers
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Figures

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Figure 1

Isometric view of office building

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Figure 2

Weekday occupancy and lighting schedule

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Figure 3

Daily energy consumption and operating cost for nighttime setup control (reference case) as a function of supply air temperature

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Figure 4

Fraction of full-load power as a function of part-load ratio

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Figure 5

Optimal control modeling: Cooling load, average zone, and operative temperature profiles for RC and three optimal scenarios with BM=3, BM=9, and BM=24 (hourly)

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Figure 6

Optimal control modeling: Daily energy consumption and operating cost for fans, lights, and chiller relative to the RC as well as corresponding total values

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Figure 7

Exhaustive search suboptimality contour plot [%].

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Figure 8

Utility rate structure: (a) cooling load, average zone, and operative temperature profiles for RC and six optimal scenarios for various utility rate ratios, and (b) daily energy consumption and operating cost for fans, lights, and chiller relative to the RC as well as corresponding total values

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Figure 9

Early peak analysis: (a) cooling load and average zone, temperature profiles for RC and optimal scenarios for 1∕3, 2∕3, 3∕3, and 4∕3 of the occupancy period, and (b) daily energy consumption and operating cost for fans, lights, and chiller relative to the RC as well as corresponding total values

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Figure 10

Lead/lag analysis: cooling load and average zone temperature profiles for RC and optimal scenarios for (a) lead times and (b) lag times

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Figure 11

Lead/lag analysis: Daily energy consumption and operating cost for RC and optimal scenarios for fans, lights, and chiller as well as corresponding total values for lead and lag times

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Figure 12

Internal gains: (a) cooling load and average zone temperature profiles for RC and five optimal scenarios for various internal gains from 5W∕m2to40W∕m2 and (b) daily energy consumption and operating cost for fans, lights, and chiller relative to the RC as well as corresponding total values

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Figure 13

Building mass: (a) Cooling load and average zone temperature profiles for RC and four optimal scenarios with mass levels in the range of 50–400% of the default value and (b) daily energy consumption and operating cost for fans, lights, and chiller relative to the RC as well as corresponding total values

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Figure 14

Weather types: (a) cooling load and average zone temperature profiles for RC and nine optimal scenarios for diurnal temperature swings in the range of 10–30K and relative humidity swings in the range of 20–60%, and (b) daily energy consumption and operating cost for fans, lights, and chiller relative to the RC as well as corresponding total values

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