Evaluation of Building Energy Performance with Optimal Control of Movable Shading Device Integrated with PV System

• Main Authors: Dong Eun Jung, Chanuk Lee, Kwang Ho Lee, Minjae Shin, Sung Lok Do
• Format: Article
• Language: English
• Published: MDPI AG 2021-03-01
• Series: Energies
• Subjects: movable shading device; photovoltaic system; artificial neural network; optimal control; window heat transfer
• Online Access: https://www.mdpi.com/1996-1073/14/7/1799

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.