Identification of passive solar design determinants in office building envelopes in hot and humid climates using data mining techniques. (June 2021)

• Record Type: Journal Article
• Authors: Qiu, Zhaoyang; Wang, Jiayuan; Yu, Bo; Liao, Longhui; Li, Jie
• Is Part Of: Building and environment. Volume 196(2021)
• Publication Date: 2021-06
• Subjects: Passive solar design -- Building energy consumption -- Data mining -- Critical factors -- Parametric energy simulation -- Office building; ARM association rule mining -- BO Building orientation -- COP coefficient of performance -- EWC exterior wall coating -- GBDT Gradient Boosting Decision Tree -- GS glazing system -- HVAC Heating, Ventilation and Air Conditioning -- HOD horizontal overhang depth -- IDF input data file -- MAE mean absolute error -- PCM phase change material -- RC roof coating -- RMSE Root mean square error -- SHGC solar thermal gain coefficient -- TMY typical meteorological year -- Tvis visible transmittance -- VFD vertical fin depth -- WWR window-to-wall -- XGBoost Extreme Gradient Boosting Decision Tree; Buildings -- Environmental engineering -- Periodicals; Building -- Research -- Periodicals; Constructions -- Technique de l'environnement -- Périodiques; Electronic journals; 696
• DOI: 10.1016/j.buildenv.2020.107566
• Languages: English
• ISSNs: 0360-1323

Abstract

Abstract: Passive solar design is an effective strategy to alleviate the energy-intensive status of the building sector. Identifying passive solar design parameters that significantly impact office buildings' energy performance can further understand sustainable design principles and prioritize energy efficiency measures. This study proposes a holistic methodology integrating data mining techniques and parametric energy simulation to explore the critical design parameters in passive solar office building envelopes in hot and humid climates. The data mining module incorporates Extreme Gradient Boosting Decision Tree (XGBoost) and association rule mining to measure feature importance and extract strong correlations, respectively. A case study, using a typical office building in Guangzhou, China as a reference building, is conducted to demonstrate the implementation procedure and feasibility of the proposed approach. In total, 115, 200 design scenarios are created and simulated in EnergyPlus software. The results of XGBoost show that the glazing system, window-to-wall ratio, and roof coating are the most critical design factors, with importance scores of 0.4858, 0.3197, and 0.1297, respectively. Similarly, based on a confidence threshold of 30% and a lift threshold of 3.0, the extracted association rules indicate that the above three factors have the strongest correlations with the energy consumption level. Findings of this study will provide practical passive solar design guidance for office buildings in hot and humid climates to achieve energy-saving targets. Also, the developed simulation-based data mining method can be applied to other building types in different climates. Graphical abstract: Image 1 Highlights: Passive solar design determinants of office building in hot climates were identified. A methodology integrating data mining and parametric energy simulation was developed. XGBoost and association rule mining were utilized in data mining. The glazing system, WWR, and roof coating were the most significant design factors. A guideline for the passive solar design of office buildings was proposed.