Conference Paper
Proceedings of BSA Conference 2024: Sixth Conference of IBPSA-Italy
Integration of Machine Learning-Based CIE Standard Skies Model With Daylight Simulation for Building Energy Performance Analysis
Integration of Machine Learning-Based CIE Standard Skies Model With Daylight Simulation for Building Energy Performance Analysis
Emmanuel Imuetinyan Aghimien, Ernest Kin-wai Tsang, Danny Hin-wa Li, Zhenyu WangDOI: https://doi.org/10.13124/9788860462022_25Abstract: Daylight illuminance data is required for daylight scheme evaluations.while the adoption of daylight-linked control (DLLC) systems is a useful strategy for attaining energy savings on lighting. For evaluations of these daylighting schemes and DLLC, determining the sky conditions through sky luminance distributions is required. Moreover, through these distributions, the 15 CIE standard skies can be identified and daylight illuminance for any surface of interest can be derived. The crucial issue is that sky luminance data is sparingly measured. Recent studies have shown that the use of accessible climatic data and machine learning (ML) models for determining the standard skies can be viable alternatives. In this study, an ensembledbased Light Gradient Boosting Machine (LGBM) was used to identify the standard sky types in Hong Kong. The predictions of the LGBM model were then integrated with RADIANCE and EnergPlus for daylight and building energy simulations of a generic shopping mall. The simulation was carried out by comparing the Best fit, ASRC 1992 and the All-weather models against the measured data. Findings show that when tested, the LGBM model correctly classified the sky types over 70% of the time. Similarly, when used for daylight and energy simulations acceptable predictions were obtained from all models. Finally, it was found that the impact of the sky luminance distribution model on illuminance prediction is higher than that for energy estimation. Pages: 191 - 198 Paper: