This study investigates the three factors that contribute to designing efficient buildings, namely technical solutions, facade systems, and occupant requirements, through the use of a real-world dataset consisting of 49 efficient buildings from various locations across the globe. Each factor comprises distinct elements that are essential in achieving building efficiency. Statistical methods, including correlation and Kruskal-Wallis methods, as well as advanced statistical methods such as the reversible jump Markov chain Monte Carlo method, were employed to estimate parameters that represent the conditional dependence between the elements of each factor. The undirected graphs were generated for each factor based on the conditional depence between the elements of the factor which is shown by a link. Through the analysis of these graphs, designers can enhance their comprehension of the correlation between the various elements of each factor, which can ultimately result in improved building efficiency. This, in turn, may lead to a decrease in air pollution and energy consumption while enhancing human comfort.
Facade Systems Human Comfort Efficient Buildings Statistical methods Reversible Jump Markov Chain Monte Carlo Method
Birincil Dil | İngilizce |
---|---|
Konular | İstatistiksel Analiz |
Bölüm | Araştırma Makalesi |
Yazarlar | |
Erken Görünüm Tarihi | 18 Temmuz 2024 |
Yayımlanma Tarihi | 18 Temmuz 2024 |
Gönderilme Tarihi | 1 Ağustos 2023 |
Kabul Tarihi | 14 Şubat 2024 |
Yayımlandığı Sayı | Yıl 2024 |