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Cluster Analysis and Model Comparison Using Smart Meter Data

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 for...

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Detalles Bibliográficos
Autores principales: Shaukat, Muhammad Arslan, Shaukat, Haafizah Rameeza, Qadir, Zakria, Munawar, Hafiz Suliman, Kouzani, Abbas Z., Mahmud, M. A. Parvez
Formato: Online Artículo Texto
Lenguaje:English
Publicado: MDPI 2021
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8124309/
https://www.ncbi.nlm.nih.gov/pubmed/34063197
http://dx.doi.org/10.3390/s21093157
Descripción
Sumario: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.