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 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

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

Introducing NASN’s New Data Initiative: National School Health Data Set: Every Student Counts! Make This YOUR Year of Data

2018 ◽  
Vol 33 (5) ◽  
pp. 291-294 ◽  
Author(s):  
Erin D. Maughan ◽  
Kathleen H. Johnson ◽  
Martha Dewey Bergren
Keyword(s):  
School Health ◽  
School Nurses ◽  
Health Data ◽  
National Association ◽  
Data Driven ◽  
School Nursing ◽  
Health Culture ◽  
Data Set ◽  
School Year ◽  
National School

The National Association of School Nurses (NASN) is launching a new data initiative: National School Health Data Set: Every Student Counts! This article describes the vision of the initiative, as well as what school nurses can do to advance a data-driven school health culture. This is the first article in a data and school nursing series for the 2018-2019 school year. For more information on NASN’s initiative and to learn how school nurses can join the data revolution, go to http://nasn.org/everystudentcounts

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.

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 Opportunities and Challenges in Data-Driven Healthcare Research

2018 ◽  
Author(s):  
You Chen
Keyword(s):  
Good Practice ◽  
High Volume ◽  
Health Data ◽  
Healthcare Research ◽  
Care Quality ◽  
Data Driven ◽  
Unplanned Readmission ◽  
Patient Centered ◽  
Smart Healthcare ◽  
Centered Care

Health information technology has been widely used in healthcare, which has contributed a huge amount of data. Health data has four characteristics: high volume; high velocity; high variety and high value. Thus, they can be leveraged to i) discover associations between genes, diseases and drugs to implement precision medicine; ii) predict diseases and identify their corresponding causal factors to prevent or control the diseases at an earlier time; iii) learn risk factors related to clinical outcomes (e.g., patients’ unplanned readmission), to improve care quality and reduce healthcare expenditure; and iv) discover care coordination patterns representing good practice in the implementation of collaborative patient-centered care. At the same time, there are major challenges existing in data-driven healthcare research, which include: i) inefficient health data exchanges across different sources; ii) learned knowledge is biased to specific institution; iii) inefficient strategies to evaluate plausibility of the learned patterns and v) incorrect interpretation and translation of the learned patterns. In this paper, we review various types of health data, discuss opportunities and challenges existing in the data-driven healthcare research, provide solutions to solve the challenges, and state the important role of the data-driven healthcare research in the establishment of smart healthcare system.

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