Establishment of optimal control strategy of building-integrated photovoltaic blind slat angle by considering interior illuminance and electricity generation

Taehoon Hong, Jeongyoon Oh, Kwangbok Jeong, Jimin Kim, Minhyun Lee

Research output: Chapter in Book/Report/Conference proceedingConference contribution

Abstract

A building-integrated photovoltaic blind (BIPB), in which blind and PV system is combined to generate energy in the building exterior and reduce the heating and cooling load in building by shading function. This study aimed to establish the optimal control strategy of BIPB slat angle by considering interior illuminance and electricity generation. First, in terms of interior illuminance considering overall light (i.e., daylight and artificial illumination) and electricity generation from BIPB, it was determined that the optimal blind slat angle is 80° at all time. Second, in terms of interior illuminance considering daylight and electricity generation from BIPB, it was determined that the optimal blind slat angle is 10° (9:00), 20° (10:00–11:00, 14:00–15:00) and 30° (12:00–13:00). Based on results of this study, effective use of BIPB can be induced by providing information for optimal blind slat angle to users that are considering BIPB implementation.

Original languageEnglish
Title of host publicationMachine Learning, Optimization, and Big Data - 2nd International Workshop, MOD 2016, Revised Selected Papers
EditorsGiuseppe Nicosia, Giovanni Giuffrida, Piero Conca, Panos M. Pardalos
PublisherSpringer Verlag
Pages451-454
Number of pages4
ISBN (Print)9783319514680
DOIs
Publication statusPublished - 2016
Event2nd International Workshop on Machine Learning, Optimization and Big Data, MOD 2016 - Volterra, Italy
Duration: 2016 Aug 262016 Aug 29

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume10122 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349

Other

Other2nd International Workshop on Machine Learning, Optimization and Big Data, MOD 2016
Country/TerritoryItaly
CityVolterra
Period16/8/2616/8/29

Bibliographical note

Funding Information:
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MSIP; Ministry of Science, ICT & Future Planning) (NRF-2015R1A2A1A05001657).

Publisher Copyright:
© Springer International Publishing AG 2016.

All Science Journal Classification (ASJC) codes

  • Theoretical Computer Science
  • Computer Science(all)

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