Alomoush, W, Khan, T B, Nadeem, M, Janjua, J I, Saeed, A and Athar, A (2022) Residential Power Load Prediction in Smart Cities using Machine Learning Approaches. In: 2022 International Conference on Business Analytics for Technology and Security (ICBATS), 16-17 Feb. 2022, Dubai, United Arab Emirates.
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Accurate load prediction plays a vital role in energy planning and load management and offers a distinctive opportunity for applying advanced analytics. Stake holders of power markets gains benefits with better integration of load management, smart grid control and metering in smart cities. It helps to improve efficiency of power load consumption. The paper proposed hybrid method based on Machine learning for predicting residential power load. We positioned correlated feature extraction and applied with system model to generate predictive results. The loss function and RMSE were calculated for accuracy of the prediction results.
Affiliation: | Skyline University College |
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SUC Author(s): | Alomoush, W ORCID: https://orcid.org/0000-0002-2937-4327 |
All Author(s): | Alomoush, W, Khan, T B, Nadeem, M, Janjua, J I, Saeed, A and Athar, A |
Item Type: | Conference or Workshop Item (Paper) |
Uncontrolled Keywords: | Smart Meters,Multiple Linear Regression,Gradient Boosting,Feature Extraction,Co-relational Trends |
Subjects: | B Information Technology > BL Machine Learning |
Divisions: | Skyline University College > School of IT |
Depositing User: | Mr Veeramani Rasu |
Date Deposited: | 27 May 2022 15:03 |
Last Modified: | 27 May 2022 15:03 |
URI: | https://research.skylineuniversity.ac.ae/id/eprint/265 |
Publisher URL: | https://doi.org/10.1109/ICBATS54253.2022.9759024 |
Publisher OA policy: | |
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