Control algorithms for dynamic windows for residential buildings

Firlag, Szymon; Yazdanian, Mehrangiz; Curcija, Charlie; Kohler, Christian; Vidanovic, Simon; Hart, Robert; Czarnecki, Stephen

doi:10.1016/j.enbuild.2015.09.069

Title: Control algorithms for dynamic windows for residential buildings

Journal Article · Wed Sep 30 00:00:00 EDT 2015 · Energy and Buildings

DOI:https://doi.org/10.1016/j.enbuild.2015.09.069· OSTI ID:1456919

Firlag, Szymon ^[1]; Yazdanian, Mehrangiz ^[2]; Curcija, Charlie ^[2]; Kohler, Christian ^[2]; Vidanovic, Simon ^[2]; Hart, Robert ^[2]; Czarnecki, Stephen ^[2]

Warsaw Univ. of Technology (Poland). Faculty of Civil Engineering
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States). Windows and Envelope Materials Group

This study analyzes the influence of control algorithms for dynamic windows on energy consumption, number of hours of retracted shades during daylight and shade operations. Five different control algorithms - heating/cooling, simple rules, perfect citizen, heat flow and predictive weather were developed and compared. The performance of a typical residential building was modeled with EnergyPlus. The program Widow was used to generate a Bi-Directional Distribution Function (BSDF) for two window configurations. The BSDF was exported to EnergyPlus using the IDF file format. The EMS feature in EnergyPlus was used to develop custom control algorithms. The calculations were made for four locations with diverse climate. The results showed that: (a) use of automated shading with proposed control algorithms can reduce the site energy in the range of 11.6-13.0%; in regard to source (primary) energy in the range of 20.1-21.6%, (b) the differences between algorithms in regard to energy savings are not high, (c) the differences between algorithms in regard to number of hours of retracted shades are visible, (e) the control algorithms have a strong influence on shade operation and oscillation of shade can occur, (d) additional energy consumption caused by motor, sensors and a small microprocessor in the analyzed case is very small.

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Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Energy Efficiency Office. Building Technologies Office

Grant/Contract Number:: AC02-05CH11231

OSTI ID:: 1456919

Journal Information:: Energy and Buildings, Vol. 109, Issue C; Related Information: © 2015 Elsevier B.V.; ISSN 0378-7788

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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Cited by: 27 works

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Model Predictive Control (MPC) for Enhancing Building and HVAC System Energy Efficiency: Problem Formulation, Applications and Opportunities Serale, Gianluca; Fiorentini, Massimo; Capozzoli, Alfonso Energies, Vol. 11, Issue 3 https://doi.org/10.3390/en11030631	journal	March 2018
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