| Autor: |
Shaukat MA; School of Engineering and Information Technology, University of Technology Sydney, Broadway, NSW 2007, Australia., Shaukat HR; Institute for Intelligent Systems Research and Innovation (IISRI), Deakin University, Geelong, VIC 3216, Australia., Qadir Z; School of Computing Engineering and Mathematics, Western Sydney University, Locked Bag 1797, Penrith, NSW 2751, Australia., Munawar HS; School of Built Environment, University of New South Wales, Kensington, Sydney, NSW 2052, Australia., Kouzani AZ; School of Engineering, Deakin University, Geelong, VIC 3216, Australia., Mahmud MAP; School of Engineering, Deakin University, Geelong, VIC 3216, Australia. |
| Abstrakt: |
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. |
