Smart-Building Applications: Deep Learning-Based, Real-Time Load Monitoring
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Date
2021
Authors
Çimen, Halil
Çetinkaya, Nurettin
Journal Title
Journal ISSN
Volume Title
Publisher
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
Open Access Color
Green Open Access
Yes
OpenAIRE Downloads
OpenAIRE Views
Publicly Funded
No
Abstract
Google's Director of Research Peter Norvig said, We don't have better algorithms than anyone else; we just have more data. This inspiring statement shows that having more data is directly related to better decision making and foresight about the future. With the development of Internet of Things (IoT) technology, it is now much easier to gather data. Technological tools, such as social media websites, smartphones, and security cameras, can be considered as data generators. When the focus is shifted to the energy field, these generators are smart meters.
Description
Keywords
Home appliances, Data models, Monitoring, Feature extraction, Smart meters, Internet of Things, Hidden Markov models, Algorithm design and theory, SYSTEM, Meters, Monitoring, Smart meters, Data models, Feature extraction, Home appliances, Hidden Markov models
Turkish CoHE Thesis Center URL
Fields of Science
0202 electrical engineering, electronic engineering, information engineering, 02 engineering and technology
Citation
WoS Q
Q1
Scopus Q
Q1

OpenCitations Citation Count
11
Source
IEEE INDUSTRIAL ELECTRONICS MAGAZINE
Volume
15
Issue
2
Start Page
4
End Page
15
PlumX Metrics
Citations
CrossRef : 4
Scopus : 13
Patent Family : 1
Captures
Mendeley Readers : 29
SCOPUS™ Citations
12
checked on Feb 03, 2026
Web of Science™ Citations
10
checked on Feb 03, 2026
Google Scholar™


