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Cluster Analysis and Model Comparison Using Smart Meter Data

Shaukat, MA, Shaukat, HR, Qadir, Z, Munawar, HS, Kouzani, Abbas and Mahmud, M A Parvez 2021, Cluster Analysis and Model Comparison Using Smart Meter Data, Sensors, vol. 21, no. 9, pp. 1-21, doi: 10.3390/s21093157.

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Title Cluster Analysis and Model Comparison Using Smart Meter Data
Author(s) Shaukat, MA
Shaukat, HR
Qadir, Z
Munawar, HS
Kouzani, AbbasORCID iD for Kouzani, Abbas orcid.org/0000-0002-6292-1214
Mahmud, M A ParvezORCID iD for Mahmud, M A Parvez orcid.org/0000-0002-1905-6800
Journal name Sensors
Volume number 21
Issue number 9
Start page 1
End page 21
Total pages 21
Publisher MDPI
Place of publication Basel, Switzerland
Publication date 2021-05-01
ISSN 1424-8220
Keyword(s) smart meter
artificial neural network
ARIMA
smart grid
regression
SGSC
Summary Load forecasting plays a crucial role in the world of smart grids. It governs many aspects of the smart grid and smart meter, such as demand response, asset management, investment, and future direction. This paper proposes time-series forecasting for short-term load prediction to unveil the load forecast benefits through different statistical and mathematical models, such as artificial neural networks, auto-regression, and ARIMA. It targets the problem of excessive computational load when dealing with time-series data. It also presents a business case that is used to analyze different clusters to find underlying factors of load consumption and predict the behavior of customers based on different parameters. On evaluating the accuracy of the prediction models, it is observed that ARIMA models with the (P, D, Q) values as (1, 1, 1) were most accurate compared to other values.
Language eng
DOI 10.3390/s21093157
Indigenous content off
Field of Research 0502 Environmental Science and Management
0602 Ecology
0301 Analytical Chemistry
0805 Distributed Computing
0906 Electrical and Electronic Engineering
HERDC Research category C1 Refereed article in a scholarly journal
Free to Read? Yes
Persistent URL http://hdl.handle.net/10536/DRO/DU:30150822

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Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.