Appliances Load Pattern Reconstruction from Adaptive Delta-Driven Sampled Smart Meter Data-Reference-Cited by-同舟云学术

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Appliances Load Pattern Reconstruction from Adaptive Delta-Driven Sampled Smart Meter Data

Published:2024-05-20 Issue: Volume: Page:1-5
ISSN:
Container-title:2024 IEEE International Instrumentation and Measurement Technology Conference (I2MTC)
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Author:

Qaisar Saeed Mian¹,López Alberto²,Kitanneh Omar³,Ferrero Francisco²

Affiliation:

1. Effat University,Electrical and Computer Engineering Department,Jeddah,Saudi Arabia,21478

2. Electronic, Communications and Systems Engineering, University of Oviedo,Department of Electrical,Gijón,Spain,33204

3. Effat University,Department of natural sciences, mathematics, and technology,Jeddah,Saudi Arabia,22332

Publisher

IEEE

Link

http://xplorestaging.ieee.org/ielx8/10560479/10560519/10560988.pdf?arnumber=10560988

Reference12 articles.

1. Deep learning for load forecasting with smart meter data: Online Adaptive Recurrent Neural Network

2. Preserving Privacy of Smart Meter Data in a Smart Grid Environment

3. Impact of battery storage on residential energy consumption: An Australian case study based on smart meter data

4. Chapter 4 A comprehensive review on the application of machine learning techniques for analyzing the smart meter data

5. Signal Piloted Processing of the Smart Meter Data for Effective Appliances Recognition

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