Authors:
Carlos Eiras-Franco
1
;
Miguel Flores
2
;
Verónica Bolón-Canedo
1
;
Sonia Zaragoza
3
;
Rubén Fernández-Casal
4
;
Salvador Naya
5
and
Javier Tarrío-Saavedra
5
Affiliations:
1
LIDIA Group, Department of Computer Science, CITIC, Universidade da Coruña, Campus de Elviña, A Coruña and Spain
;
2
Department of Mathematics, Escuela Politécnica Nacional, Quito and Ecuador
;
3
PROTERM Group, Department of Naval and Industrial Engineering, Escola Politécnica Superior, Universidade da Coruña, Mendizábal s/n, Ferrol, Spain, Σqus company, Oleiros and Spain
;
4
MODES Group, Department of Mathematics, Facultade de Informática, Universidade da Coruña, Campus de Elviña, A Coruña, Spain, Centro de Investigación TIC (CITIC), Universidade da Coruña, Campus de Elviña, A Coruña and Spain
;
5
Centro de Investigación TIC (CITIC), Universidade da Coruña, Campus de Elviña, A Coruña, Spain, MODES Group, Department of Mathematics, Escola Politécnica Superior, Universidade da Coruña, Mendizábal s/n, Ferrol and Spain
Keyword(s):
Statistical Quality Control, Anomaly Detection, Feature Selection, Energy Efficiency, HVAC, Industry 4.0, LOCI, ReliefF, Functional Data Analysis.
Related
Ontology
Subjects/Areas/Topics:
Applications
;
Artificial Intelligence
;
Biomedical Engineering
;
Biomedical Signal Processing
;
Business Analytics
;
Business Intelligence
;
Change Detection
;
Data Analytics
;
Data Engineering
;
Data Manipulation
;
Data Mining
;
Databases and Information Systems Integration
;
Datamining
;
Enterprise Information Systems
;
Health Engineering and Technology Applications
;
Health Information Systems
;
Human-Computer Interaction
;
Informatics in Control, Automation and Robotics
;
Knowledge Discovery and Information Retrieval
;
Knowledge-Based Systems
;
Methodologies and Methods
;
Neurocomputing
;
Neurotechnology, Electronics and Informatics
;
Pattern Recognition
;
Physiological Computing Systems
;
Sensor Networks
;
Signal Processing
;
Signal Processing, Sensors, Systems Modeling and Control
;
Soft Computing
;
Software Engineering
;
Statistics Exploratory Data Analysis
;
Symbolic Systems
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 clothin
g 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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