Energy demand forecasting using fused machine learning approaches

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Date
2022
Authors
Ghazal, Taher M.
Noreen, Sajida
Said, Raed A.
Khan, Muhammad Adnan
Siddiqui, Shahan Yamin
Abbas, Sagheer
Aftab, Shabib
Ahmad, Munir
Journal Title
Journal ISSN
Volume Title
Publisher
Tech Science Press
Abstract
The usage of IoT-based smart meter in electric power consumption shows a significant role in helping the users to manage and control their electric power consumption. It produces smooth communication to build equitable electric power distribution for users and improved management of the entire electric system for providers. Machine learning predicting algorithms have been worked to apply the electric efficiency and response of progressive energy creation, trans-mission, and consumption. In the proposed model, an IoT-based smart meter uses a support vector machine and deep extreme machine learning techniques for professional energy management. A deep extreme machine learning approach applied to feature-based data provided a better result. Lastly, decision-based fusion applied to both datasets to predict power consumption through smart meters and get better results than previous techniques. The established model smart meter with automatic load control increases the effectiveness of energy management. The proposed EDF-FMLA model achieved 90.70 accuracy for predicting energy consumption with a smart meter which is better than the existing approaches. © 2022, Tech Science Press. All rights reserved.
Description
This article is licensed under Creative Commons License and full text is openly accessible in CUD Digital Repository. The version of the scholarly record of this article is published in Intelligent Automation and Soft Computing (2022), available online at: https://doi.org/doi:10.32604/IASC.2022.019658
Keywords
Decision-based fusion , Deep extreme learning , EDF-FMLA , Energy , Feature fusion , Smart meters , SVM
Citation
Ghazal, T. M., Noreen, S., Said, R. A., Khan, M. A., Siddiqui, S. Y., Abbas, S., . . . Ahmad, M. (2022). Energy demand forecasting using fused machine learning approaches. Intelligent Automation and Soft Computing, 31(1), 539-553. https://doi.org/doi:10.32604/IASC.2022.019658
URI
https://doi.org/doi:10.32604/IASC.2022.019658
 http://hdl.handle.net/20.500.12519/446
DOI
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International Business