A Model-Driven Learning Approach for Predicting the Personalized Dynamic Thermal Comfort in Ordinary Office Environment

Author(s):  
Yadong Zhou ◽  
Xukun Wang ◽  
Zhanbo Xu ◽  
Ying Su ◽  
Ting Liu ◽  
...  
2021 ◽  
Vol 238 ◽  
pp. 110790
Author(s):  
Yadong Zhou ◽  
Ying Su ◽  
Zhanbo Xu ◽  
Xukun Wang ◽  
Jiang Wu ◽  
...  

Energies ◽  
2019 ◽  
Vol 12 (10) ◽  
pp. 1835 ◽  
Author(s):  
Arman Ameen ◽  
Mathias Cehlin ◽  
Ulf Larsson ◽  
Taghi Karimipanah

A vital requirement for all-air ventilation systems are their functionality to operate both in cooling and heating mode. This article experimentally investigates two newly designed air distribution systems, corner impinging jet (CIJV) and hybrid displacement ventilation (HDV) in comparison against a mixing type air distribution system. These three different systems are examined and compared to one another to evaluate their performance based on local thermal comfort and ventilation effectiveness when operating in heating mode. The evaluated test room is an office environment with two workstations. One of the office walls, which has three windows, faces a cold climate chamber. The results show that CIJV and HDV perform similar to a mixing ventilation in terms of ventilation effectiveness close to the workstations. As for local thermal comfort evaluation, the results show a small advantage for CIJV in the occupied zone. Comparing C2-CIJV to C2-CMV the average draught rate (DR) in the occupied zone is 0.3% for C2-CIJV and 5.3% for C2-CMV with the highest difference reaching as high as 10% at the height of 1.7 m. The results indicate that these systems can perform as well as mixing ventilation when used in offices that require moderate heating. The results also show that downdraught from the windows greatly impacts on the overall airflow and temperature pattern in the room.


2007 ◽  
Vol 42 (12) ◽  
pp. 4022-4027 ◽  
Author(s):  
Yoshifumi Murakami ◽  
Masaaki Terano ◽  
Kana Mizutani ◽  
Masayuki Harada ◽  
Satoru Kuno

2021 ◽  
Vol 2042 (1) ◽  
pp. 012070
Author(s):  
Tobias Kramer ◽  
Veronica Garcia-Hansen ◽  
Sara Omrani Vahid M. Nik ◽  
Dong Chen

Abstract This paper presents an alternative workflow for thermal comfort prediction. By using the leverage of Data Science & AI in combination with the power of computational design, the proposed methodology exploits the extensive comfort data provided by the ASHRAE Global Thermal Comfort Database II to generate more customised comfort prediction models. These models consider additional, often significant input parameters like location and specific building characteristics. Results from an early case study indicate that such an approach has the potential for more accurate comfort predictions that eventually lead to more efficient and comfortable buildings.


2022 ◽  
pp. 1586-1611
Author(s):  
Alexandre Bragança ◽  
Isabel Azevedo ◽  
Nuno Bettencourt

Model-driven engineering (MDE) is an approach to software engineering that adopts models as the central artefact. Although the approach is promising in addressing major issues in software development, particularly in dealing with software complexity, and there are several success cases in the industry as well as growing interest in the research community, it seems that it has been hard to generalize its gains among software professionals. To address this issue, MDE must be taught at a higher-education level. This chapter presents a three-year experience in teaching MDE in a course of a master program in informatics engineering. The chapter provides details on how a project-based learning approach was adopted and evolved along three editions of the course. Results of a student survey are discussed and compared to those from another course. In addition, several other similar teaching experiences are analyzed.


2021 ◽  
pp. 108056
Author(s):  
Bouziane Brik ◽  
Moez Esseghir ◽  
Leila Merghem-Boulahia ◽  
Hichem Snoussi

Sign in / Sign up

Export Citation Format

Share Document