Anomaly Detection in Public Street Lighting Data Using Unsupervised Clustering-Reference-Cited by-同舟云学术

Anomaly Detection in Public Street Lighting Data Using Unsupervised Clustering

Published:2024-02 Issue:1 Volume:70 Page:4524-4535
ISSN:0098-3063
Container-title:IEEE Transactions on Consumer Electronics
language:
Short-container-title:IEEE Trans. Consumer Electron.

Author:

Ali Mubashir¹^ORCID,Scandurra Patrizia²^ORCID,Moretti Fabio³^ORCID,Sherazi Hafiz Husnain Raza⁴^ORCID

Affiliation:

1. Computer Science Department, University of Birmingham (Edgbaston), Birmingham, U.K.

2. Department of Engineering and Applied Sciences, University of Bergamo, Dalmine, Italy

3. Energy Technologies and Renewable Sources Department, ENEA National Agency for New Technologies Energy and Economic Sustainable Development, Rome, Italy

4. School of Computing, Newcastle University, Newcastle Upon Tyne, U.K.

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Link

http://xplorestaging.ieee.org/ielx7/30/10510045/10400552.pdf?arnumber=10400552

Reference35 articles.

1. Machine learning based system for managing energy efficiency of public sector as an approach towards smart cities

2. Building energy-consumption status worldwide and the state-of-the-art technologies for zero-energy buildings during the past decade

3. Types of Machine Learning Algorithms

4. Unsupervised Anomaly Detection for Energy Consumption in Time Series using Clustering Approach

5. Architecting a big data-driven software architecture for smart street lighting

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