Assessing thermal comfort and energy efficiency in buildings by statistical quality control for autocorrelated data

Barbeito, Inés; Zaragoza, Sonia; Tarrío-Saavedra, Javier; Naya, Salvador

doi:10.1016/j.apenergy.2016.12.100

Cited by 34 publications

(12 citation statements)

References 39 publications

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“…The proposed method can be integrated to these control policies and can enhance them by providing online data about personal thermal comfort requirements. However, more research is required to understand how control paradigms based on presentation of uncomfortable states can be optimized as any control action may have either negative or positive energy consumption consequences, depending on the building, climate, and other influential characteristics [14,[44][45][46][47][48]. Through our investigations, we demonstrated that there is a tradeoff between the accuracy for detecting uncomfortable conditions and the overall ratio of comfortable conditions, implying that more constraints would be imposed on an HVAC system when preventing uncomfortable conditions has a higher importance.…”

Section: Discussionmentioning

confidence: 99%

Towards unsupervised learning of thermal comfort using infrared thermography

Ghahramani¹,

Castro²,

Karvigh³

et al. 2018

Applied Energy

138

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Maintaining thermal comfort in built environments is important for occupant health, well-being, and productivity, and also for efficient HVAC system operations. Most of the existing personal thermal comfort learning methods require occupants to provide feedback via a survey to label the monitored environmental or physiological conditions in order to train the prediction models. However, the accuracy of these models usually drops after the training process as personal thermal comfort is dynamic and changes over time due to climatic variations and/or acclimation. In this paper, we present a hidden Markov model (HMM) based learning method to capture personal thermal comfort using infrared thermography of human face. We chose human face since its blood vessels has a higher density and it is not covered while performing regular activities in built environments. Learning algorithm has 3 hidden states (i.e., uncomfortably warm, comfortable, uncomfortably cool) and uses discretization for forming the observed states from the continuous infrared measurements. The approach can potentially be used for continuous monitoring of thermal comfort to capture the variations over time. We tested and validated the method in a fourday long experiment with 10 subjects and demonstrated an accuracy of 82.8% for predicting uncomfortable conditions.

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Section: Discussionmentioning

confidence: 99%

Towards unsupervised learning of thermal comfort using infrared thermography

Ghahramani¹,

Castro²,

Karvigh³

et al. 2018

Applied Energy

138

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“…The data continuously monitored over time by different sensors usually show a variable level of autocorrelation (the greater the correlation the closer the observations are in time). The application of standard techniques in the case of the violation of the independence hypothesis often results in the detection of an unacceptable number of false alarms [34]. Therefore, the development and analysis of techniques that remove the sample autocorrelation is fully justified.…”

Section: Alternatives Of the Statistical Quality Control When The Basmentioning

confidence: 99%

“…Therefore, the development and analysis of techniques that remove the sample autocorrelation is fully justified. Within these techniques, the most widespread is the application of time series models (e.g., autoregressive moving average (ARMA) and ARIMA) to remove the correlation between observations and the subsequent monitoring of the error variable (difference between actual values and those estimated by the model) using control charts [34][35][36]. Moreover, References [37,38] propose the combination of control charts with adjustment algorithms.…”

Section: Alternatives Of the Statistical Quality Control When The Basmentioning

confidence: 99%

Constructing a Control Chart Using Functional Data

et al. 2020

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This study proposes a control chart based on functional data to detect anomalies and estimate the normal output of industrial processes and services such as those related to the energy efficiency domain. Companies providing statistical consultancy services in the fields of energy efficiency; heating, ventilation and air conditioning (HVAC); installation and control; and big data for buildings, have been striving to solve the problem of automatic anomaly detection in buildings controlled by sensors. Given the functional nature of the critical to quality (CTQ) variables, this study proposed a new functional data analysis (FDA) control chart method based on the concept of data depth. Specifically, it developed a control methodology, including the Phase I and II control charts. It is based on the calculation of the depth of functional data, the identification of outliers by smooth bootstrap resampling and the customization of nonparametric rank control charts. A comprehensive simulation study, comprising scenarios defined with different degrees of dependence between curves, was conducted to evaluate the control procedure. The proposed statistical process control procedure was also applied to detect energy efficiency anomalies in the stores of a textile company in the Panama City. In this case, energy consumption has been defined as the CTQ variable of the HVAC system. Briefly, the proposed methodology, which combines FDA and multivariate techniques, adapts the concept of the control chart based on a specific case of functional data and thereby presents a novel alternative for controlling facilities in which the data are obtained by continuous monitoring, as is the case with a great deal of process in the framework of Industry 4.0.

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“…Thus, statistical and machine learning techniques that automate the procedures of anomaly detection and quality control of products and services are increasingly needed (Lee et al, 2014). Specifically, the companies of building energy efficiency sector have recently developed energy web platforms that require the implementation of statistical tools to automate the anomaly detection, the predictive maintenance, and the quality control of building installa-tions (Barbeito et al, 2017;Flores et al, 2018). That is the case of web platform developed by Σqus company, that provides the real case study described in this work.…”

Section: Introductionmentioning

confidence: 99%

Case Study of Anomaly Detection and Quality Control of Energy Efficiency and Hygrothermal Comfort in Buildings

Eiras-Franco

Flores

Bolón-Canedo

et al. 2019

Proceedings of the 8th International Conference on Data Science, Technology and Applications

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The aim of this work is to propose different statistical and machine learning methodologies for identifying anomalies and control the quality of energy efficiency and hygrothermal comfort in buildings. Companies focused on energy sector for buildings are interested on statistical and machine learning tools to automate the control of energy consumption and ensure quality of Heat Ventilation and Air Conditioning (HVAC) installations. Consequently, a methodology based on the application of the Local Correlation Integral (LOCI) anomaly detection technique has been proposed. In addition, the most critical variables for anomaly detection are identified by using ReliefF method. Once vectors of critical variables are obtained, multivariate and univariate control charts can be applied to control the quality of HVAC installations (consumption, thermal comfort). In order to test the proposed methodology, the companies involved in this project have provided the case study of a store of a clothing brand located in a shopping center in Panama. It is important to note that this is a controlled case study for which all the anomalies have been previously identified by maintenance personnel. Moreover, as an alternatively solution, in addition to machine learning and multivariate techniques, new nonparametric control charts for functional data based on data depth have been proposed and applied to curves of daily energy consumption in HVAC.

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Assessing thermal comfort and energy efficiency in buildings by statistical quality control for autocorrelated data

Cited by 34 publications

References 39 publications

Towards unsupervised learning of thermal comfort using infrared thermography

Towards unsupervised learning of thermal comfort using infrared thermography

Constructing a Control Chart Using Functional Data

Case Study of Anomaly Detection and Quality Control of Energy Efficiency and Hygrothermal Comfort in Buildings

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