Evaluation of building energy performance with optimal control of movable shading device integrated with pv system

Dong Eun Jung, Chanuk Lee, Kwang Ho Lee, Minjae Shin, Sung Lok Do

Research output: Contribution to journalArticlepeer-review

5 Citations (Scopus)


Among the envelope components (e.g., walls, roofs, floors, and windows, etc.) affecting the cooling and heating load of buildings, windows are the most thermally vulnerable. Shading devices can minimize the thermal load on windows due to solar radiation and decrease radiation effects. However, the load changes due to convection and conduction should be considered. Therefore, when a shading device is applied to a window, control logic for thermal blocking and heat retention is necessary to prevent the load changes. In addition, by combining the opposite features of photovoltaic (PV) that require solar radiation and the shading device to block solar radiation, energy-saving and production can be achieved simultaneously. Therefore, this study minimized the thermal effects of windows using a movable shading device integrated with PV and evaluated the PV power generation. This study evaluated the effects on window heat transfer by applying artificial intelligence techniques, which have recently attracted attention, to system operation. To achieve this, artificial neural network (ANN)-based control logic was developed, and the control performance of the system was assessed using simulations. In ANN control, the window heat transfer was 86.3% lower in a cooling period and 9.7% lower in a heating period compared with that of a shading device fixed at 45. Furthermore, the PV system produced 3.0 to 3.1% more electric power under optimal control during the cooling period.

Original languageEnglish
Article number1799
Issue number7
Publication statusPublished - 2021 Apr 1

Bibliographical note

Funding Information:
Funding: This work was supported by the Korea Institute of Energy Technology Evaluation and Planning (KETEP) and the Ministry of Trade, Industry and Energy (MOTIE) of the Republic of Korea (No. 20204030200080). This research was also supported by the research fund of Hanbat National University in 2019.

Publisher Copyright:
© 2021 by the authors. Licensee MDPI, Basel, Switzerland.


  • Artificial neural network
  • Movable shading device
  • Optimal control
  • Photovoltaic system
  • Window heat transfer

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment
  • Fuel Technology
  • Energy Engineering and Power Technology
  • Energy (miscellaneous)
  • Control and Optimization
  • Electrical and Electronic Engineering


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