Optimal Control Strategies for Switchable Transparent Insulation Systems Applied to Smart Windows for US Residential Buildings

This paper evaluates the potential energy use and peak demand savings associated with optimal controls of switchable transparent insulation systems (STIS) applied to smart windows for US residential buildings. The optimal controls are developed based on Genetic Algorithm (GA) to identify the automat...

Full description

Bibliographic Details
Main Authors: Mohammad Dabbagh, Moncef Krarti
Format: Article
Language:English
Published: MDPI AG 2021-05-01
Series:Energies
Subjects:
Online Access:https://www.mdpi.com/1996-1073/14/10/2917
id doaj-0f4f3d08bdfe46b998a2b786903f20be
record_format Article
spelling doaj-0f4f3d08bdfe46b998a2b786903f20be2021-06-01T00:21:58ZengMDPI AGEnergies1996-10732021-05-01142917291710.3390/en14102917Optimal Control Strategies for Switchable Transparent Insulation Systems Applied to Smart Windows for US Residential BuildingsMohammad Dabbagh0Moncef Krarti1Civil, Environmental, and Architectural Engineering Department, University of Colorado Boulder, Boulder, CO 80309, USACivil, Environmental, and Architectural Engineering Department, University of Colorado Boulder, Boulder, CO 80309, USAThis paper evaluates the potential energy use and peak demand savings associated with optimal controls of switchable transparent insulation systems (STIS) applied to smart windows for US residential buildings. The optimal controls are developed based on Genetic Algorithm (GA) to identify the automatic settings of the dynamic shades. First, switchable insulation systems and their operation mechanisms are briefly described when combined with smart windows. Then, the GA-based optimization approach is outlined to operate switchable insulation systems applied to windows for a prototypical US residential building. The optimized controls are implemented to reduce heating and cooling energy end-uses for a house located four US locations, during three representative days of swing, summer, and winter seasons. The performance of optimal controller is compared to that obtained using simplified rule-based control sets to operate the dynamic insulation systems. The analysis results indicate that optimized controls of STISs can save up to 81.8% in daily thermal loads compared to the simplified rule-set especially when dwellings are located in hot climates such as that of Phoenix, AZ. Moreover, optimally controlled STISs can reduce electrical peak demand by up to 49.8% compared to the simplified rule-set, indicating significant energy efficiency and demand response potentials of the SIS technology when applied to US residential buildings.https://www.mdpi.com/1996-1073/14/10/2917genetic algorithmsoptimal controlsenergy efficiencypeak demandresidential buildingsswitchable insulation systems
collection DOAJ
language English
format Article
sources DOAJ
author Mohammad Dabbagh
Moncef Krarti
spellingShingle Mohammad Dabbagh
Moncef Krarti
Optimal Control Strategies for Switchable Transparent Insulation Systems Applied to Smart Windows for US Residential Buildings
Energies
genetic algorithms
optimal controls
energy efficiency
peak demand
residential buildings
switchable insulation systems
author_facet Mohammad Dabbagh
Moncef Krarti
author_sort Mohammad Dabbagh
title Optimal Control Strategies for Switchable Transparent Insulation Systems Applied to Smart Windows for US Residential Buildings
title_short Optimal Control Strategies for Switchable Transparent Insulation Systems Applied to Smart Windows for US Residential Buildings
title_full Optimal Control Strategies for Switchable Transparent Insulation Systems Applied to Smart Windows for US Residential Buildings
title_fullStr Optimal Control Strategies for Switchable Transparent Insulation Systems Applied to Smart Windows for US Residential Buildings
title_full_unstemmed Optimal Control Strategies for Switchable Transparent Insulation Systems Applied to Smart Windows for US Residential Buildings
title_sort optimal control strategies for switchable transparent insulation systems applied to smart windows for us residential buildings
publisher MDPI AG
series Energies
issn 1996-1073
publishDate 2021-05-01
description This paper evaluates the potential energy use and peak demand savings associated with optimal controls of switchable transparent insulation systems (STIS) applied to smart windows for US residential buildings. The optimal controls are developed based on Genetic Algorithm (GA) to identify the automatic settings of the dynamic shades. First, switchable insulation systems and their operation mechanisms are briefly described when combined with smart windows. Then, the GA-based optimization approach is outlined to operate switchable insulation systems applied to windows for a prototypical US residential building. The optimized controls are implemented to reduce heating and cooling energy end-uses for a house located four US locations, during three representative days of swing, summer, and winter seasons. The performance of optimal controller is compared to that obtained using simplified rule-based control sets to operate the dynamic insulation systems. The analysis results indicate that optimized controls of STISs can save up to 81.8% in daily thermal loads compared to the simplified rule-set especially when dwellings are located in hot climates such as that of Phoenix, AZ. Moreover, optimally controlled STISs can reduce electrical peak demand by up to 49.8% compared to the simplified rule-set, indicating significant energy efficiency and demand response potentials of the SIS technology when applied to US residential buildings.
topic genetic algorithms
optimal controls
energy efficiency
peak demand
residential buildings
switchable insulation systems
url https://www.mdpi.com/1996-1073/14/10/2917
work_keys_str_mv AT mohammaddabbagh optimalcontrolstrategiesforswitchabletransparentinsulationsystemsappliedtosmartwindowsforusresidentialbuildings
AT moncefkrarti optimalcontrolstrategiesforswitchabletransparentinsulationsystemsappliedtosmartwindowsforusresidentialbuildings
_version_ 1721415061179727872