Thermal Comfort Control in Air-Conditioned Buildings: new data-driven approaches to Neutral Temperature estimation

2019 ◽  
Author(s):  
Federica Acerbi ◽  
Giuseppe De Nicolao ◽  
Mirco Rampazzo
Keyword(s):  
Thermal Comfort ◽  
Data Driven ◽  
Temperature Estimation ◽  
Neutral Temperature

A hybrid physics-based/data-driven model for personalized dynamic thermal comfort in ordinary office environment

2021 ◽  
Vol 238 ◽  
pp. 110790
Author(s):  
Yadong Zhou ◽  
Ying Su ◽  
Zhanbo Xu ◽  
Xukun Wang ◽  
Jiang Wu ◽  
...  
Keyword(s):  
Thermal Comfort ◽  
Data Driven ◽  
Office Environment

scholarly journals How Can We Adapt Thermal Comfort for Disabled Patients? A Case Study of French Healthcare Buildings in Summer

Energies ◽  
2021 ◽  
Vol 14 (15) ◽  
pp. 4530
Author(s):  
Youcef Bouzidi ◽  
Zoubayre El Akili ◽  
Antoine Gademer ◽  
Nacef Tazi ◽  
Adil Chahboun
Keyword(s):  
Thermal Comfort ◽  
Thermal Environment ◽  
Residential Buildings ◽  
Linear Regression Analysis ◽  
Environmental Parameters ◽  
Physical Parameters ◽  
Mean Radiant Temperature ◽  
Operative Temperature ◽  
Neutral Temperature ◽  
Radiant Temperature

This paper investigates adaptive thermal comfort during summer in medical residences that are located in the French city of Troyes and managed by the Association of Parents of Disabled Children (APEI). Thermal comfort in these buildings is evaluated using subjective measurements and objective physical parameters. The thermal sensations of respondents were determined by questionnaires, while thermal comfort was estimated using the predicted mean vote (PMV) model. Indoor environmental parameters (relative humidity, mean radiant temperature, air temperature, and air velocity) were measured using a thermal environment sensor during the summer period in July and August 2018. A good correlation was found between operative temperature, mean radiant temperature, and PMV. The neutral temperature was determined by linear regression analysis of the operative temperature and Fanger’s PMV model. The obtained neutral temperature is 23.7 °C. Based on the datasets and questionnaires, the adaptive coefficient α representing patients’ capacity to adapt to heat was found to be 1.261. A strong correlation was also observed between the sequential thermal index n(t) and the adaptive temperature. Finally, a new empirical model of adaptive temperature was developed using the data collected from a longitudinal survey in four residential buildings of APEI in summer, and the obtained adaptive temperature is 25.0 °C with upper and lower limits of 24.7 °C and 25.4 °C.

Data-driven thermal comfort model via support vector machine algorithms: Insights from ASHRAE RP-884 database

2020 ◽  
Vol 211 ◽  
pp. 109795 ◽  
Author(s):  
Xiang Zhou ◽  
Ling Xu ◽  
Jingsi Zhang ◽  
Bing Niu ◽  
Maohui Luo ◽  
...  
Keyword(s):  
Support Vector Machine ◽  
Thermal Comfort ◽  
Data Driven ◽  
Support Vector ◽  
Thermal Comfort Model

scholarly journals Data-driven study on individual occupant comfort using heating setpoints and window openings in new low-energy apartments – preliminary insights

2019 ◽  
Vol 111 ◽  
pp. 04063 ◽  
Author(s):  
Lucile Sarran ◽  
Morten Herget Christensen ◽  
Christian Anker Hviid ◽  
Andrea Marin Radoszynski ◽  
Carsten Rode ◽  
...  
Keyword(s):  
Residential Building ◽  
Heating System ◽  
Influential Factors ◽  
Low Energy ◽  
Structured Interviews ◽  
Temperature Estimation ◽  
Neutral Temperature ◽  
Window Opening ◽  
Occupant Comfort ◽  
Valve Opening

This work suggests a method to evaluate residential building occupants’ neutral temperature in winter based on their interaction with their heating system. This study applies the developed method on eight new, low-energy apartments in Copenhagen, Denmark. A set of indoor temperature, heating setpoint, window opening and floor heating valve opening data was collected from mid-January to the end of April, spanning through a large part of the Danish heating season. Semi-structured interviews were performed with occupants of three of the eight apartments in order to understand their use of their heating system. This preliminary study permits to highlight the potential and the current limitations of the proposed method, both for neutral temperature estimation as such and for applications in optimizing the energy flexibility provided by the building. This article suggests directions for further elaboration of the model. The main two influential factors highlighted here affecting setpoint adjustment are the occupants’ acceptability of temperature variation and their ability to control the heating system.

scholarly journals Development of a health data-driven model for a thermal comfort study

2020 ◽  
Vol 177 ◽  
pp. 106874
Author(s):  
Yi Jiang ◽  
Zhe Wang ◽  
Borong Lin ◽  
Dejan Mumovic
Keyword(s):  
Thermal Comfort ◽  
Health Data ◽  
Data Driven

A Data-Driven Thermal Sensation Model Based Predictive Controller for Indoor Thermal Comfort and Energy Optimization

2014 ◽  
Author(s):  
Xiao Chen ◽  
Qian Wang
Keyword(s):  
Thermal Comfort ◽  
Thermal Sensation ◽  
Energy Optimization ◽  
Data Driven ◽  
Air Velocity ◽  
Comfort Index ◽  
Indoor Thermal Comfort ◽  
Occupant Feedback ◽  
Predictive Controller ◽  
Radiant Temperature

This paper proposes a model predictive controller (MPC) using a data-driven thermal sensation model for indoor thermal comfort and energy optimization. The uniqueness of this empirical thermal sensation model lies in that it uses feedback from occupants (occupant actual votes) to improve the accuracy of model prediction. We evaluated the performance of our controller by comparing it with other MPC controllers developed using the Predicted Mean Vote (PMV) model as thermal comfort index. The simulation results demonstrate that in general our controller achieves a comparable level of energy consumption and comfort while eases the computation demand posed by using the PMV model in the MPC formulation. It is also worth pointing out that since we assume that our controller receives occupant feedback (votes) on thermal comfort, we do not need to monitor the parameters such as relative humidity, air velocity, mean radiant temperature and occupant clothing level changes which are necessary in the computation of PMV index. Furthermore simulations show that in cases where occupants’ actual sensation votes might deviate from the PMV predictions (i.e., a bias associated with PMV), our controller has the potential to outperform the PMV based MPC controller by providing a better indoor thermal comfort.

Thermal comfort analyses of naturally ventilated university classrooms

2016 ◽  
Vol 34 (4/5) ◽  
pp. 427-445 ◽  
Author(s):  
Baharuddin Hamzah ◽  
Muhammad Taufik Ishak ◽  
Syarif Beddu ◽  
Mohammad Yoenus Osman
Keyword(s):  
Thermal Comfort ◽  
Air Temperature ◽  
Thermal Environment ◽  
Thermal Sensation ◽  
National Standard ◽  
Content Type ◽  
Thermal Environments ◽  
Neutral Temperature ◽  
Radiant Temperature ◽  
University Classrooms

Purpose The purpose of this paper is to analyse thermal comfort and the thermal environment in naturally ventilated classrooms. Specifically, the aims of the study were to identify the thermal environment and thermal comfort of respondents in naturally ventilated university classrooms and compare them with the ASHRAE and Indonesian National Standard (SNI); to check on whether the predicted mean vote (PMV) model is applicable or not for predicting the thermal comfort of occupants in naturally ventilated university classrooms; and to analyse the neutral temperature of occupants in the naturally ventilated university classrooms. Design/methodology/approach The study was carried out at the new campus of Faculty of Engineering, Hasanuddin University, Gowa campus. A number of field surveys, which measured thermal environments, namely, air temperature, mean radiant temperature (MRT), relative humidity, and air velocity, were carried out. The personal activity and clothing properties were also recorded. At the same time, respondents were asked to fill a questionnaire to obtain their thermal sensation votes (TSV) and thermal comfort votes (TCV), thermal preference, and thermal acceptance. A total of 118 respondents participated in the study. Before the survey was conducted, a brief explanation was provided to the participants to ensure that they understood the study objectives and also how to fill in the questionnaires. Findings The results indicated that the surveyed classrooms had higher thermal environments than those specified in the well-known ASHRAE standard and Indonesian National Standard (SNI). However, this condition did not make respondents feel uncomfortable because a large proportion of respondents voted within the comfort zone (+1, 0, and −1). The predictive mean vote using the PMV model was higher than the respondents’ votes either by TSV or by TCV. There was a huge difference between neutral temperature using operative temperature (To) and air temperature (Ta). This difference may have been because of the small value of MRT recorded in the measured classrooms. Originality/value The research shows that the use of the PMV model in predicting thermal comfort in the tropic region might be misleading. This is because PMV mostly overestimates the TSV and TCV of the respondents. People in the tropic region are more tolerant to a higher temperature. On the basis of this finding, there is a need to develop a new thermal comfort model for university classrooms that is particularly optimal for this tropical area.

scholarly journals Smart System for Thermal Comfort Prediction on Residential Buildings Using Data-Driven Model with Random Forest Classifier

2021 ◽  
Vol 5 (4) ◽  
pp. 40-45
Author(s):  
O. E. Taylor ◽  
P. S. Ezekiel ◽  
V. T. Emma
Keyword(s):  
Feature Extraction ◽  
Random Forest ◽  
Thermal Comfort ◽  
Residential Buildings ◽  
Random Forest Classifier ◽  
Data Driven ◽  
Smart System ◽  
Building Area ◽  
Using Data ◽  
Testing Set

Building area is a vital consumer of all globally produced energy. Structures of buildings absorb about 40 % of the total energy created which transcription about 30 % of the integral worldwide CO2 radiations. As such, reducing the measure of energy absorbed by the building area would incredibly help the much-crucial depletions in world energy utilization and the related ecological concerns. This paper presents a smart system for thermal comfort prediction on residential buildings using data driven model with Random Forest Classifier. The system starts by acquiring a global thermal comfort data, pre-processed the acquired data, by removing missing values and duplicated values, and also reduced the numbers of features in the dataset by selecting just twelve columns out of 70 columns in total. This process is called feature extraction. After the pre-processing and feature extraction, the dataset was split into a training and testing set. The training set was 70% while the testing set was 30% of the original dataset. The training data was used in training our thermal comfort model with Random Forest Classifier. After training, Random Forest Classifier had an accuracy of 99.99% which is about 100% approximately. We then save our model and deployed to web through python flask, so that users can use it in predicting real time thermal comfort in their various residential buildings.

scholarly journals An adaptive thermal comfort model for naturally ventilated classrooms of technical institutions in Madurai

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Subhashini S. ◽  
Thirumaran Kesavaperumal ◽  
Masa Noguchi
Keyword(s):  
Thermal Comfort ◽  
Natural Ventilation ◽  
Higher Learning ◽  
Educational Institutions ◽  
Climatic Region ◽  
Content Type ◽  
Comfort Levels ◽  
Neutral Temperature ◽  
Adaptive Comfort ◽  
Temperature Levels

Purpose Occupants dwelling in hot climatic regions of India for a longer term are tolerable to high temperature levels than predicted by American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) standards. The purpose of this study is to evaluate the thermal sensations (TS) and neutral temperature of the occupants in naturally ventilated (NV) and air-conditioned (AC) classrooms of two technical institutions located in the same premises in the suburbs of Madurai. The main focus of this study is to understand the occupants’ behaviour in response to the thermal conditions of the educational buildings particularly in the warm and humid climatic zone of Madurai. Design/methodology/approach This research collected data through field studies. The data included 383 survey questionnaires from NV classrooms and 285 from AC classrooms, as well as on-site measurements of interior and exterior weather conditions. The TS results show that the students preferred well-designed NV classrooms than AC classrooms. A new adaptive comfort equation derived from this study can be applied to NV classrooms in warm and humid climates where mean outdoor temperature exceeds 40°C. Findings The neutral temperature derived for NV classrooms in Madurai ranged from 29°C to 34°C. Thus, the occupants in the NV classrooms of the higher learning educational institutions in the warm and humid climatic region of Madurai can adapt well to higher indoor temperature levels than predicted by ASHRAE comfort levels with minimum adjustments. Research limitations/implications The study was limited to only occupants in two premier higher learning technical educational institutions located in Madurai region within 5–10 km within the city limits to understand the implications of microclimate with respect to the urban context. Thus, further research is required to examine the tendency under local conditions in other regions beyond those applied to this study. Social implications The findings of this study showed that occupants in higher learning educational intuitions in Madurai prefer NV classrooms than AC classrooms. Therefore, with rising demands of energy use for mechanical ventilation and the associated high cost for running AC buildings, architects should prioritize the design of energy efficient buildings through the optimal use of passive design strategies for ventilation and thermal comfort. This study gives a base data for architects to understand the adaptive limitations of occupants and design NV buildings that can promote natural ventilation and provide better thermal environments that can help increase the productivity of students. Originality/value This paper was an attempt to develop the adaptive comfort model for NV classrooms in Madurai regions. There has been no attempt to identify the adaptive comfort levels of occupants in higher learning technical educational institutions located in warm and humid climatic region of India.

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