Thermal comfort in hospitals – A literature review

Khodakarami, Jamal; Nasrollahi, Nazanin

doi:10.1016/j.rser.2012.03.054

Cited by 133 publications

(68 citation statements)

References 13 publications

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“…Other studies in hospital settings were carried out by Wang et al [354] in Taiwan and by van Gaever et al [355] in Belgium. Khodakarami and Nasrollahi reviewed the literature on thermal comfort in hospitals [356]. An overview of thermal comfort for older people with dementia was presented by [357].…”

Section: Hospitals Healthcare Facilities and Elderly Centersmentioning

confidence: 99%

A review of human thermal comfort in the built environment

Rupp

Vásquez

Lamberts

2015

Energy and Buildings

662

282

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Section: Hospitals Healthcare Facilities and Elderly Centersmentioning

confidence: 99%

A review of human thermal comfort in the built environment

Rupp

Vásquez

Lamberts

2015

Energy and Buildings

662

282

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“…natural light, noise), the other from the integrated review papers presented in Table 1. Note that as Table 1 has integrated all relevant findings, the sub-design/design (Khodakarami & Nasrollahi, 2012) CEI Air quality (Sundell, 2004) Air quality (Jones, 1999 …”

Section: Preliminary Conceptual Frameworkmentioning

confidence: 99%

Healing built-environment effects on health outcomes: environment–occupant–health framework

Zhang

Tzortzopoulos

Kagioglou

2018

Building Research & Information

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An investigation examined the structured scientific evidence on healthcare facilities (the healing built environment -HBE) and its impact on patients' health outcomes under a holistic conceptual evaluative framework. The integrative review considered 127 papers (of which 59 were review papers). It found there was no adequate framework that could integrate existing research findings holistically. Such a holistic framework needs to demonstrate the cumulative and interactive effects of various HBE characteristics on patients' health outcomes and wellbeing. An environment-occupant-health (E-O-H) framework is proposed, taking a holistic perspective to identify and evaluate different HBE characteristics. The E-O-H framework should support future research by (1) identifying the HBE characteristics that affect health outcomes; (2) defining appropriate future research designs; and (3) understanding the need for holistic analysis of the integrated effects of diverse HBE characteristics on health outcomes.

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“…Large Medical Centers have specific features which clearly distinguish them from other kinds of buildings and difficult the energy modeling tasks: 1) a continuous energy and electricity utilization is required; 2) difficulties in measuring detailed building occupancy; 3) complex and energy-intensive facilities (surgeries rooms, TAC machines) are usually subject to unpredictable events; and 4) restrictive requirements of air quality and indoor comfort temperature. Besides, any energy saving initiatives must preserve healthcare delivery [3,5,6].…”

Section: Introductionmentioning

confidence: 99%

Energy consumption modeling by machine learning from daily activity metering in a hospital

Ruiz

Pacheco-Torres

Casillas

2017

2017 22nd IEEE International Conference on Emerging Technologies and Factory Automation (ETFA)

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Hospitals are large buildings that consume a great amount of energy mostly due to their continuous energy consumption needs, high consumer medical equipment, and special requirements of thermal and air conditions. Reliable dynamic simulation is a chimera because of the complex design and behavior of these buildings. Therefore, monitoring-based methods arise as a plausible alternative. Its main drawback, however, is the lack of enough data to generate statistically robust models.The paper faces this problem thanks to the helpful contribution of a collaborative hospital which was able to generate daily data of electrical energy consumption for a period of six years. Besides, thirteen variables that summarize the daily activity of the hospital are also included. The results show how machine learning techniques generate models that accurately predict the electrical energy consumption based on weather conditions and activity measurements.The obtained results are useful for the design of more specific energy saving strategies, a more efficient economic investment for energy retrofitting of existing buildings and a better management of economic energy cost in large-scale buildings.

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Thermal comfort in hospitals – A literature review

Cited by 133 publications

References 13 publications

A review of human thermal comfort in the built environment

A review of human thermal comfort in the built environment

Healing built-environment effects on health outcomes: environment–occupant–health framework

Energy consumption modeling by machine learning from daily activity metering in a hospital

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