Smart-Building Applications: Deep Learning-Based, Real-Time Load Monitoring

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Date

2021

Journal Title

Journal ISSN

Volume Title

Publisher

IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC

Open Access Color

Green Open Access

Yes

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OpenAIRE Views

Publicly Funded

No
Impulse
Top 10%
Influence
Average
Popularity
Top 10%

Research Projects

Journal Issue

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
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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

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Google Scholar™
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1.27665684

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