An End to End Real Time Architecture for Analyzing and Clustering Time Series Data: Case of an Energy Management System

Talei, Hanaa; Essaaidi, Mohamed; Benhaddou, Driss

doi:10.1109/irsec.2018.8702839

Cited by 8 publications

(2 citation statements)

References 14 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Machine learning algorithms can be split into two main categories: (1) supervised learning, which involves making predictions with the help of labeled datasets (for instance, given the geometrical measurement of a house and its location, a supervised algorithm could predict its price); and (2) unsupervised learning, which involves using unlabeled data to extract some information/patterns (for instance, finding out the probability of the co-occurrence of items in a collection [33]). Besides associations, unsupervised learning is also involves solving clustering problems meant to divide data into groups, where every group contains data with the same behavior.…”

Section: K-means Clustering Algorithm: a Step-by-step Processmentioning

confidence: 99%

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

et al. 2021

Self Cite

View full text Add to dashboard Cite

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.

show abstract

Section: K-means Clustering Algorithm: a Step-by-step Processmentioning

confidence: 99%

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

et al. 2021

Self Cite

View full text Add to dashboard Cite

show abstract

“…An experiment on ten-time segments showed that the obtained clusters were effective, in which both the whole similarity and the trend similarity on training data were markedly higher than that of randomized clustering [11]. Hanaa Talei et al presented an end-to-end real-time architecture for analyzing and clustering time-series sensor data using an IoT architecture [12]. The authors used the Euclidian distance to compute the distance between the time-series and AHC to cluster time-series.…”

Section: Introductionmentioning

confidence: 99%

Customer Behavior Clustering Based on Balance History Using Dynamic Time Warping Distance

Hung¹,

Dat²

2020

IJMLC

View full text Add to dashboard Cite

Customer clustering, the division of customers into different groups, is a classical problem. It is especially important in banking as it serves multiple purposes in marketing, risk management, etc. Therefore, it has attracted the use of many modern machine learning models and techniques. But currently, most of them are only making use of "static" customer information. This paper proposes a new approach for customer clustering in banking based on the customers' balance history. Basic Dynamic Time Warping (DTW) distance and Soft-DTW (SDTW) distance, an advanced form, are used to measure the difference between customers. To which, the two most popular strategies in time series clustering strategies, partitional and hierarchical, are applied which. In additional, some statistical features are given to prove the effectiveness of the proposed method.

show abstract

Clustering of Time-Series Balance History Data Streams Using Apache Spark

Dat

Hung

2020

Lecture Notes in Computer Science

View full text Add to dashboard Cite

An End to End Real Time Architecture for Analyzing and Clustering Time Series Data: Case of an Energy Management System

Cited by 8 publications

References 14 publications

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

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

Customer Behavior Clustering Based on Balance History Using Dynamic Time Warping Distance

Clustering of Time-Series Balance History Data Streams Using Apache Spark

Contact Info

Product

Resources

About