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Cluster Analysis and Model Comparison Using Smart Meter Data

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journal contribution
posted on 2021-05-01, 00:00 authored by Muhammad Arslan ShaukatMuhammad Arslan Shaukat, Haafizah Rameeza ShaukatHaafizah Rameeza Shaukat, Zakria Qadir, Hafiz Suliman Munawar, Abbas KouzaniAbbas Kouzani, M A Parvez Mahmud
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.

History

Journal

Sensors

Volume

21

Issue

9

Pagination

1 - 21

Publisher

MDPI

Location

Basel, Switzerland

eISSN

1424-8220

Language

eng

Publication classification

C1 Refereed article in a scholarly journal