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

• DOI: 10.5220/0007839701450151
• Publication Date: 2019-08-09T05:19:53Z
• Authors: Carlos Eiras-Franco; Miguel Flores; Verónica Bolón-Ca...; Sonia Zaragoza; Rubén Fernández-C...; Salvador Naya; Javier Tarrío-Saa...
• Keywords: ReliefF; Energy Efficiency; LOCI; Anomaly detection; Industry 4.0; HVAC; 7. Clean energy; 01 natural sciences; Functional data analysis; Energy efficiency; 13. Climate action; Feature selection; Functional Data Analysis; 11. Sustainability; Anomaly Detection; Feature Selection; 0101 mathematics; Statistical Quality Control

[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. Ministerio de Asuntos Económicos y Transformación Digital; MTM2014-52876-R Ministerio de Asuntos Económicos y Transformación Digital; MTM2017-82724-R Xunta de Galicia; ED431C-2016-015 Centro Singular de Investigación de Galicia; ED431G/01 2016-19 Centro de Investigación en Tecnoloxías da Información e as Comunicacións da Universidade da Coruña; PC18/03 Escuela Politécnica Nacional of Ecuador; PII-DM-002-2016