Long-term monitoring for indoor climate assessment – The association between objective and subjective data

Loomans, Mglc Marcel; Mishra, Asit Kumar; Kooi, L

doi:10.1016/j.buildenv.2020.106978

Cited by 8 publications

(6 citation statements)

References 28 publications

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“…• Limitations: The challenge lies in the significant resources required for data management and analysis. Additionally, there is a potential for data errors and inconsistencies due to sensor calibration issues or environmental interference [22].…”

Section: Long-term Monitoringmentioning

confidence: 99%

Machine Learning Techniques in Indoor Environmental Quality Assessment

Kumar Gajendran,

Fazil Syed Ahmed Kabir,

Vadivelu

et al. 2024

Civil Engineering

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This chapter provides a comprehensive exploration of the evolving role of machine learning in Indoor Environmental Quality (IEQ) assessment. As urban living spaces become increasingly enclosed, the importance of maintaining optimal IEQ for human health and well-being has surged. Traditional methods for IEQ assessment, while effective, often fail to provide real-time monitoring and control. This gap is increasingly being addressed by the integration of machine learning techniques, allowing for enhanced predictive modeling, real-time optimization, and robust anomaly detection. The chapter delves into a comparative analysis of various machine learning techniques including supervised, unsupervised, and reinforcement learning, demonstrating their unique benefits in IEQ assessment. Practical implementations of these techniques in residential, commercial, and specialized environments are further illustrated through detailed case studies. The chapter also addresses the existing challenges in implementing machine learning for IEQ assessment and provides an outlook on future trends and potential research directions. The comprehensive review offered in this chapter encourages continued innovation and research in leveraging machine learning. for more efficient and effective IEQ assessment.

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Section: Long-term Monitoringmentioning

confidence: 99%

Machine Learning Techniques in Indoor Environmental Quality Assessment

Kumar Gajendran,

Fazil Syed Ahmed Kabir,

Vadivelu

et al. 2024

Civil Engineering

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“…However, there are national [1] and international [2-6] standards that establish the technical and methodological bases to estimate these thermal indicators based on the local particularities of each case. At the same time, different authors have developed universal thermal comfort models [7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22][23][24] of graphic, mathematical, computer, or algorithmic type, which offer an approach to local thermal comfort [25][26][27][28][29][30][31][32][33][34] from outdoor weather and some endogenous factors of people (activity level and clothing).…”

Section: Introductionmentioning

confidence: 99%

Indoor Thermal Comfort from the Estimation Thermal Environment’s Physical Variables in Temperate-Dry Bioclimate

Rincón-Martínez¹,

Anda²,

Fernández-Melchor³

2023

Cooling Technologies - Technologies and Systems to Guarantee Thermal Comfort in Efficient Buildings

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Adverse thermal conditions alter the indoor habitability and consequently, the occupants' health, performance, mood and comfort. Although there are local regulations that provide thermal indicators for the indoor architectural design, this are usually unrelated to the climate, type of construction project and psychophysiological adaptation of people. In this way, this chapter shows the thermal comfort ranges estimated from the thermal environment’s physical variables, using the adaptive approach, for Ensenada City, Mexico (dry-temperate bioclimate). Surveys were applied to collect the subjective perception simultaneously of the measurement of black globe temperature, air temperature, relative humidity and wind speed. Study sample was made up from students of a public university whose activity level is sedentary (1.2 met) and clothing is light (0.7 clo). The survey was designed based on the ISO 10551 and ANSI/ASHRAE 55 standards, while the physical measurement instruments were selected based on ISO 7726, managing to generate a class I database. The study was correlational and statistically analyzed with 3,750 surveys from Average by Thermal Sensation Intervals method. 16 comfort ranges were quantitatively and graphically estimated from the four physical variables analyzed in each of the four representative thermal periods of the city. These indicators offer objective knowledge for proper decision-making during the architectural design process and, therefore, for the thermal-energy efficiency of buildings.

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“…Monitoring indoor climate in office buildings has its background in legal obligations for providing healthy working environments (e.g., minimum and maximum temperatures) on the one hand, and the monetary interest of an employer to profit from high productivity by a stimulating working environment without thermal stress and insufficient air quality on the other [6]. Monitoring the indoor climate is a precondition to interpret the energy consumption data of a building correctly [7][8][9].…”

Section: Introductionmentioning

confidence: 99%

Indoor Climate Monitoring in Office Buildings—Comparative Analysis of Two Office Buildings without Air Conditioning

Voss,

Voß,

Kaliga

2023

Energies

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Against the background of climate protection and the rising costs of a fossil-fuel-based energy supply, the interest in the energy performance and indoor climate of buildings in real operation is rising. This paper, therefore, deals with the indoor climate investigation of two medium-sized office buildings in Germany by taking measurements over a whole year. These relate to one new building and one refurbished building. Sensors of various types were installed and operated in a large number of office rooms, so that in total results are available for over 100 rooms, typically occupied by one or two persons. The analysis focuses on the indoor temperature in summer and the air quality in winter based on the CO2 concentration. The comfort classes according to DIN EN 16798 including the adaptive comfort approach are used as a basis to cluster the results. Both buildings have movable sun protection and openable windows but no facilities for active cooling. They, thus, represent a large number of existing ‘low tech’ office buildings in Germany and central Europe. The results reflect the respective building concepts but also show a wide range between the rooms due to the user preferences and behaviour. The refurbished building shows better results, especially in terms of air quality but also in terms of summer room temperatures. This underlines the benefit of the targeted measures as a result of an analysis of the deficits in the existing building before the refurbishment. The additional measures for decentralised mechanical ventilation and passive cooling are having positive effects. As part of the projects, further measures to improve the indoor climate were investigated in both buildings. In one case, this involved CO2 traffic lights to stimulate personal window ventilation in winter, and in the other, the use of newly developed individual ceiling fans supports convective heat dissipation on the human body during hot spells in summer. The positive effect could be demonstrated for both measures.

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Long-term monitoring for indoor climate assessment – The association between objective and subjective data

Cited by 8 publications

References 28 publications

Machine Learning Techniques in Indoor Environmental Quality Assessment

Machine Learning Techniques in Indoor Environmental Quality Assessment

Indoor Thermal Comfort from the Estimation Thermal Environment’s Physical Variables in Temperate-Dry Bioclimate

Indoor Climate Monitoring in Office Buildings—Comparative Analysis of Two Office Buildings without Air Conditioning

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