ANN based automatic slat angle control of venetian blind for minimized total load in an office building

Sanghun Yeon, Byeongho Yu, Byeongmo Seo, Yeobeom Yoon, Kwang Ho Lee

Research output: Contribution to journalArticlepeer-review

40 Citations (Scopus)


Windows are the only part of a building that can directly penetrate the solar radiation into the occupied space and thus the shading devices are needed to control the solar penetration. A variety of research have been conducted to develop the optimized slat angle control in the existing literature, but the research incorporating artificial intelligence technique with slat angle control is limited thus far. Therefore, in this study, the ANN (Artificial Neural Network) model was applied to minimize the combined total load consisting of lighting, cooling, and heating loads through automatic slat angle control of venetian blinds. A three-story rectangular office building was simulated using EnergyPlus, and dimming control was applied to control the lighting. The interlocked simulation between Matlab and EnergyPlus was conducted through BCVTB. As a result of comparing automatic blind control via the ANN to fixed blind slat angle, the automatic blind control via the ANN showed 9.1% lower total load than the blind angle fixed at 50°. It was confirmed that the cooling and heating load could be significantly reduced by real-time automatic control via the ANN under various operating conditions, rather than fixing the blinds at one angle.

Original languageEnglish
Pages (from-to)133-145
Number of pages13
JournalSolar Energy
Publication statusPublished - 2019 Mar 1
Externally publishedYes

Bibliographical note

Funding Information:
This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (Ministry of Science and ICT) (No. 2015R1A1A1A05000964 ).


  • ANN (Artificial Neural Network)
  • Daylight
  • EnergyPlus
  • Matlab
  • Slat angle
  • Venetian blind

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment
  • General Materials Science


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