Shading devices are used to control solar radiations that penetrate into the occupied environment through the windows with the aim of ensuring visual comfort and saving the building’s energy consumption. Venetian blinds are commonly employed for the practicality and ease of application. However, occupants often do not change slat orientation causing unnecessary consumption and discomfort. Hence, automatic shading control systems can enhance the energy performance and make the environment more comfortable. In this context, a cognitive venetian blinds system, denoted to as CogVBS and based on a deep feed-forward neural network, is proposed for automatic estimation of slat angle. Here, the EnergyPlus software is employed to simulate the test environment. Experimental results demonstrate the promising performance of the proposed deep CogVBS, reporting a root mean square error (RMSE) and correlation coefficient (r) of 0.1018±0.0015 and 0.9319±0.0020, respectively. The achieved outcomes encourage the use of the proposed cognitive system in realistic environments.
A Deep Cognitive Venetian Blinds System for Automatic Estimation of Slat Orientation
Nicoletti F.;Arcuri N.;
2022-01-01
Abstract
Shading devices are used to control solar radiations that penetrate into the occupied environment through the windows with the aim of ensuring visual comfort and saving the building’s energy consumption. Venetian blinds are commonly employed for the practicality and ease of application. However, occupants often do not change slat orientation causing unnecessary consumption and discomfort. Hence, automatic shading control systems can enhance the energy performance and make the environment more comfortable. In this context, a cognitive venetian blinds system, denoted to as CogVBS and based on a deep feed-forward neural network, is proposed for automatic estimation of slat angle. Here, the EnergyPlus software is employed to simulate the test environment. Experimental results demonstrate the promising performance of the proposed deep CogVBS, reporting a root mean square error (RMSE) and correlation coefficient (r) of 0.1018±0.0015 and 0.9319±0.0020, respectively. The achieved outcomes encourage the use of the proposed cognitive system in realistic environments.I documenti in IRIS sono protetti da copyright e tutti i diritti sono riservati, salvo diversa indicazione.
