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An Expandable, Contextualized and Data-Driven Indoor Thermal Comfort Model

Sajjadian, Seyed Masoud; Jafari, Mina; Pekaslan, Direnc


Mina Jafari

Direnc Pekaslan


Continuous discrepancies in building performance predictions creates an ongoing inclination to link contextualized, real-time inputs and users’ feedback for not only building control systems but also for simulation tools. It is now seeming necessary to develop a model that can record, find meaningful relationship and predict more holistic human interactions in buildings. Such model could create capacity for feedback and control with a level of intelligence. Fuzzy Logic Systems (FLSs) are known as robust tools in decision making and developing models in an efficient manner. Considering this capability, in this paper, FLSs is implemented to make a thermal comfort model in an educational building in the UK. Such implementation has an ability to respond to some identified desires of developers and performance assessors in addressing uncertainty in thermal comfort models. The results demonstrate the proposed method is practical to simulate the value of comfort level based on the input data.


Sajjadian, S. M., Jafari, M., & Pekaslan, D. (2020). An Expandable, Contextualized and Data-Driven Indoor Thermal Comfort Model. Energy and Built Environment, 1(4), 385-392.

Journal Article Type Article
Acceptance Date Apr 11, 2020
Online Publication Date May 11, 2020
Publication Date 2020-10
Deposit Date Nov 19, 2020
Publicly Available Date Nov 20, 2020
Journal Energy and Built Environment
Publisher Elsevier
Peer Reviewed Peer Reviewed
Volume 1
Issue 4
Pages 385-392
Keywords Fuzzy Logic, Thermal Comfort Model ,Artificial Intelligence
Public URL


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