Development and validation of a low-cost infrared measurement system for real-time monitoring of indoor thermal comfort

Revel, Gian Marco; Arnesano, Marco; Pietroni, Filippo

doi:10.1088/0957-0233/25/8/085101

Cited by 36 publications

(11 citation statements)

References 12 publications

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“…Activation of specific sensors with established positions has previously been used in passive infrared [60,61,62,63,64,65,66], acoustic [46,76], and WiFi signal [27,46,47,48,49,50] to obtain occupancy and location details using a heterogeneous sensing network. In these studies, a multimodal data fusion and deep learning method were employed to estimate occupancy.…”

Section: Literature Reviewmentioning

confidence: 99%

Comparative Analysis of Machine Learning Methods for Non-Intrusive Indoor Occupancy Detection and Estimation

Aliero¹,

Pasha²,

Toosi³

et al. 2021

Preprint

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Occupancy-driven application research has been active research for a decade that focuses on improving or replacing new building infrastructure to improve building energy efficiency. Existing approaches for HVAC energy saving are putting more emphasis on occupancy detection, estimation, and localization to trade-off between energy consumption and thermal comfort satisfaction. In a non-intrusive approach, various sensors, actuators, and analytic data methods are commonly used to process data from occupant surroundings and trigger appropriate action to achieve the task. However, the performance of the non-intrusive approach reported in the literature is relatively poor due to the lack of quality of dataset used in model training and expropriate choice of machine learning model. This study proposed a non-intrusive approach that to improve the collection and quality of dataset using data pre-processing. The study collected a training dataset using various sensors installed in the building and developed a model using five machine learning models to determine occupant’s presence and estimate their number in the building. The proposed solution is tested in the living room with a prototype system integrated with various sensors designed to obtain occupant surrounding environmental datasets. The model’s prediction results obtained indicate that it is possible for the proposed solution to obtain data, process, and predict the occupant number with high accuracy (73.6 -99.7% using random forest).

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Section: Literature Reviewmentioning

confidence: 99%

Comparative Analysis of Machine Learning Methods for Non-Intrusive Indoor Occupancy Detection and Estimation

Aliero¹,

Pasha²,

Toosi³

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…The measuring concept is based on the thermal scanning of indoor room surfaces to calculate the mean radiant temperature with the angle factors method, according to ISO 7726, in function of the room geometry and occupants location. The detailed description of the methodology is presented in (Revel et al 2014). The mean radiant temperature is merged with the data (air temperature and relative humidity) acquired by a second sensing node that is placed in the most representative zone of the room.…”

Section: Comfort Eyementioning

confidence: 99%

IEQ measurement and assessment tools for Plug-and-Play deep renovation in buildings

Arnesano¹,

Revel²,

Sebastian³

et al. 2018

Healthy, Intelligent and Resilient Buildings and Urban Environments

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This paper presents the approach developed for the monitoring and assessment of Indoor Environmental Quality (IEQ) in the whole deep renovation process aimed at reducing energy consumptions and improving comfort. The research was performed by the P2Endure project, that aims to provide scalable, adaptable and ready-to-implement prefabricated Plug-and-Play systems for deep renovation of building envelopes and technical systems. The idea is to use IEQ as one of the design criteria supporting the decision-making process, included into the holistic renovation process developed by P2Endure and called "4M: Mapping-Modelling-Making-Monitoring". For this reason, a set of Key Performance Indicators (KPI) was selected, with the consequent measurement and calculation methodologies. The KPIs are collected and showed together with the analysis of the different IEQ dimensions (thermal and indoor air quality). The data collection has been investigated extensively, taking into account all possible data sources, measurements and surveys (e.g. special questionnaires for children in schools). In addition to traditional devices, the innovative Comfort Eye sensor is used in the proposed framework. This is a low-cost sensing system capable of measuring thermal comfort together with IAQ, applicable for permanent or periodic monitoring and with low disturbance for inhabitants. The overall procedure is presented, also in relation with the deep renovation process. Then, the application of the measurement and assessment tools in real demonstration cases is illustrated with initial results from the monitoring campaign.

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“…Following the same approach, the indoor thermal conditions have to be measured with the same granularity, which is not feasible using the traditional thermostat. For this reason, the methodology described in [5], based on the Comfort Eye sensor [6], has been applied. The sensing facades and Comfort Eye are then integrated into a BMS system with embedded software that allows the optimal control.…”

Section: General Description Of the Proposed Systemmentioning

confidence: 99%

“…To optimize the control of the HVAC and façade systems, the fine-grained measurement of the radiant temperatures is required. An example was presented in [5] where an innovative IR-based thermal comfort sensor [6] was used to measure thermal comfort in two subzones of an office room to control two heaters separately and to provide heat only where needed because of the different comfort requirements caused by the glazed surface. This work presents the application of that approach to a meeting room in a curtain wall building and integrated with a BMS to apply the sub-zonal control of the HVAC and façade systems.…”

Section: Introductionmentioning

confidence: 99%

A Sub-Zonal PMV-Based HVAC and Façade Control System for Curtain Wall Buildings

Arnesano

Calvaresi

Pietroni

et al. 2018

Sustainable Places 2018

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This paper presents an experimental test room in a curtain wall building where an innovative monitoring and control system was implemented and tested. The proposed solution is composed by an IR-based comfort sensor that measures the PMV (Predicted Mean Vote) index for 2 room’s sub-zones and provides the optimal air temperature set-points. The overall control system includes a distributed sensors and actuators network, also embedded into the façade modules, to measure indoor and outdoor parameters and to regulate fan-coils, windows opening and shadings with a sub-zonal approach. Initial results turn out to provide an energy saving of about 20% with an improvement of thermal/visual comfort and IAQ conditions.

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Development and validation of a low-cost infrared measurement system for real-time monitoring of indoor thermal comfort

Cited by 36 publications

References 12 publications

Comparative Analysis of Machine Learning Methods for Non-Intrusive Indoor Occupancy Detection and Estimation

Comparative Analysis of Machine Learning Methods for Non-Intrusive Indoor Occupancy Detection and Estimation

IEQ measurement and assessment tools for Plug-and-Play deep renovation in buildings

A Sub-Zonal PMV-Based HVAC and Façade Control System for Curtain Wall Buildings

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