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 and Sung Lok Do
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Dong Eun Jung: Department of Building and Plant Engineering, Hanbat National University, 125, Dongseo-daero, Yuseong-gu, Daejeon 34158, Korea
Chanuk Lee: Department of Building and Plant Engineering, Hanbat National University, 125, Dongseo-daero, Yuseong-gu, Daejeon 34158, Korea
Kwang Ho Lee: Department of Architecture, College of Engineering, Korea University, 145 Anam-ro, Seongbuk-gu, Seoul 02841, Korea
Minjae Shin: Architecture and Architectural Engineering, Hanyang University, 55, Hanyangdaehak-ro, Sangnok-gu, Ansan 15588, Korea
Sung Lok Do: Department of Building and Plant Engineering, Hanbat National University, 125, Dongseo-daero, Yuseong-gu, Daejeon 34158, Korea

Energies, 2021, vol. 14, issue 7, 1-21

Abstract: 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.

Keywords: movable shading device; photovoltaic system; artificial neural network; optimal control; window heat transfer (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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