Smart Building Energy Inefficiencies Detection through Time Series Analysis and Unsupervised Machine Learning

Hanaa Talei, Driss Benhaddou, Carlos Gamarra, Houda Benbrahim and Mohamed Essaaidi
Additional contact information
Hanaa Talei: Smart Systems Lab, ENSIAS, Mohammed V University in Rabat, Rabat 10000, Morocco
Driss Benhaddou: Department of Computer Engineering Technology, University of Houston, Houston, TX 77204, USA
Carlos Gamarra: Houston Advanced Research Center, Houston, TX 77381, USA
Houda Benbrahim: Department of Computer Science and Decision Support, Ecole Nationale Supérieure d’Informatique et d’Analyse des Systèmes, Rabat 10112, Morocco
Mohamed Essaaidi: College of Engineering, Ecole Nationale Supérieure d’Informatique et d’Analyse des Systèmes, Rabat 10112, Morocco

Energies, 2021, vol. 14, issue 19, 1-21

Abstract: The climate of Houston, classified as a humid subtropical climate with tropical influences, makes the heating, ventilation, and air conditioning (HVAC) systems the largest electricity consumers in buildings. HVAC systems in commercial buildings are usually operated by a centralized control system and/or an energy management system based on a fixed schedule and scheduled control of a zone setpoint, which is not appropriate for many buildings with changing occupancy rates. Lately, as part of energy efficiency analysis, attention has focused on collecting and analyzing smart meters and building-related data, as well as applying supervised learning techniques, to propose new strategies to operate HVAC systems and reduce energy consumption. On the other hand, unsupervised learning techniques have been used to study the consumption information and profile characterization of different buildings after cluster analysis is performed. This paper adopts a different approach by revealing the power of unsupervised learning to cluster data and unveiling hidden patterns. In this study, we also identify energy inefficiencies after exploring the cluster results of a single building’s HVAC consumption data and building usage data as part of the energy efficiency analysis. Time series analysis and the K-means clustering algorithm are successfully applied to identify new energy-saving opportunities in a highly efficient office building located in the Houston area (TX, USA). The paper uses 1-year data from a highly efficient Leadership in Energy and Environment Design (LEED)-, Energy Star-, and Net Zero-certified building, showing a potential energy savings of 6% using the K-means algorithm. The results show that clustering is instrumental in helping building managers identify potential additional energy savings.

Keywords: smart building; LEED building; energy efficiency; unsupervised learning; clustering; time series; IoT (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2021
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

Downloads: (external link)
https://www.mdpi.com/1996-1073/14/19/6042/pdf (application/pdf)
https://www.mdpi.com/1996-1073/14/19/6042/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:14:y:2021:i:19:p:6042-:d:641088

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().