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

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.

Provide a Method Improving Temperature Control in Smart Buildings Based in Slicing Technique and Clustering Iot Network Based on Composition

“…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 (Talei, H., Benhaddou, D., Gamarra, C., Benbrahim, H., & Essaaidi, M., 2021).…”

Development of an Algorithm for Regulating the Load Schedule of Educational Institutions Based on the Forecast of Electric Consumption within the Framework of Application of the Demand Response

“…For most consumers, the energy plan is directly related to the production plan, therefore, optimal planning of the load schedule is necessary so that the damage from load transfer does not exceed the positive economic effect from participation in the demand response program [55,56]. In the case of an educational institution, this plan is the conducted classes, for which it is possible to establish the cost of conducting in relation to the costs and discounts for participation in demand response.…”

Digital Twins for Intelligent Green Buildings

“…Intelligent building energy saving is a new science that is part of the green energy-saving building branch, and the original professional division of labor is different. Many professional areas, such as architecture, construction, heating and ventilation, are included in smart architecture, which is formed by the intersection and combination of many professional disciplines [12][13][14]. The analysis and evaluation of intelligent buildings should follow the green building energy-saving principle.…”