Machine Learning Approach Electric Appliance Consumption and Peak Demand Forecasting of Residential Customers Using Smart Meter Data-Reference-Cited by-同舟云学术

Machine Learning Approach Electric Appliance Consumption and Peak Demand Forecasting of Residential Customers Using Smart Meter Data

Published:2020-01-14 Issue:1 Volume:111 Page:65-82
ISSN:0929-6212
Container-title:Wireless Personal Communications
language:en
Short-container-title:Wireless Pers Commun

Author:

Abera Fikirte Zemene^ORCID,Khedkar Vijayshri

Publisher

Springer Science and Business Media LLC

Subject

Electrical and Electronic Engineering,Computer Science Applications

Link

http://link.springer.com/content/pdf/10.1007/s11277-019-06845-6.pdf

Reference27 articles.

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2. Kwac, J., Flora, J., & Rajagopal, R. (2014). Household energy consumption segmentation using hourly data. IEEE Transactions on Smart Grid, 5(1), 420–430.

3. Lu, H., Li, B. M., & Wei, H. (2012, June). A small-world of neuronal functional network from multi electrode recordings during a working memory task. In The 2012 international joint conference on neural networks (IJCNN) (pp. 1–6). IEEE.

4. www.ijcaonline.org/archives/volume180/number6/zemene-2017-ijca-916052.pdf.

5. Martinez-Pabon, M., Eveleigh, T., & Tanju, B. (2017). Smart meter data analytics for optimal customer selection in demand response programs. Energy Procedia,107, 49–59.

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