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Title	Residential Power Load Prediction In Smart Cities Using Machine Learning Approaches
ID_Doc	45985
Authors	Alomoush W.; Khan T.A.; Nadeem M.; Janjua J.I.; Saeed A.; Athar A.
Year	2022
Published	2022 International Conference on Business Analytics for Technology and Security, ICBATS 2022
DOI	http://dx.doi.org/10.1109/ICBATS54253.2022.9759024
Abstract	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. © 2022 IEEE.
Author Keywords	Co-relational Trends; Feature Extraction; Gradient Boosting; Multiple Linear Regression; Smart Meters

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