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

Carlos Eiras-Franco, Miguel Flores, Verónica Bolón-Canedo, Sonia Zaragoza, Rubén Fernández-Casal, Salvador Naya, Javier Tarrío-Saavedra

2019

Abstract

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.

Paper Citation

in Harvard Style

Eiras-Franco C., Flores M., Bolón-Canedo V., Zaragoza S., Fernández-Casal R., Naya S. and Tarrío-Saavedra J. (2019). Case Study of Anomaly Detection and Quality Control of Energy Efficiency and Hygrothermal Comfort in Buildings.In Proceedings of the 8th International Conference on Data Science, Technology and Applications - Volume 1: DATA, ISBN 978-989-758-377-3, pages 145-151. DOI: 10.5220/0007839701450151

in Bibtex Style

@conference{data19,
author={Carlos Eiras-Franco and Miguel Flores and Verónica Bolón-Canedo and Sonia Zaragoza and Rubén Fernández-Casal and Salvador Naya and Javier Tarrío-Saavedra},
title={Case Study of Anomaly Detection and Quality Control of Energy Efficiency and Hygrothermal Comfort in Buildings},
booktitle={Proceedings of the 8th International Conference on Data Science, Technology and Applications - Volume 1: DATA,},
year={2019},
pages={145-151},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0007839701450151},
isbn={978-989-758-377-3},
}

in EndNote Style

TY - CONF

JO - Proceedings of the 8th International Conference on Data Science, Technology and Applications - Volume 1: DATA,
TI - Case Study of Anomaly Detection and Quality Control of Energy Efficiency and Hygrothermal Comfort in Buildings
SN - 978-989-758-377-3
AU - Eiras-Franco C.
AU - Flores M.
AU - Bolón-Canedo V.
AU - Zaragoza S.
AU - Fernández-Casal R.
AU - Naya S.
AU - Tarrío-Saavedra J.
PY - 2019
SP - 145
EP - 151
DO - 10.5220/0007839701450151