Energy Demand Forecasting Using Fused Machine Learning Approaches

Ghazal, T M, Noreen, S, Said, R A, Khan, M A, Siddiqui, S Y, Abbas, S, Aftab, S and Ahmad, M (2022) Energy Demand Forecasting Using Fused Machine Learning Approaches. Intelligent Automation & Soft Computing, 31 (1). pp. 539-553. ISSN 2326-005X

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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, transmission, 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.

Affiliation: Skyline University College
SUC Author(s): Ghazal, T M ORCID:
All Author(s): Ghazal, T M, Noreen, S, Said, R A, Khan, M A, Siddiqui, S Y, Abbas, S, Aftab, S and Ahmad, M
Item Type: Article
Uncontrolled Keywords: Feature fusion; deep extreme learning; SVM; decision-based fusion; smart meters; energy; EDF-FMLA
Subjects: B Information Technology > BL Machine Learning
Divisions: Skyline University College > School of IT
Depositing User: Mr Veeramani Rasu
Date Deposited: 29 Mar 2022 12:27
Last Modified: 18 Jan 2024 07:28
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