Big Data and Machine Learning-Based Iot Models for Sustainable Energy Prediction

Sravani, Parvathareddy and Vinitha, Kanakambaran (2024) Big Data and Machine Learning-Based Iot Models for Sustainable Energy Prediction. Journal of Innovation and Technology, 2024 (20). pp. 1-8. ISSN 2805-5179

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Abstract

Integrating Big Data and Internet of Things (IoT) platforms is the focus of this research, which aims to improve energy management. The problem statement is centered on the potential for development through advanced technologies and the inefficiencies in traditional energy management methods. The objectives are to analyze energy consumption patterns, develop an innovative Home Energy Management System (HEMS) architecture, and offer energy-saving solutions. Synthetic energy consumption data is generated, normalized, and divided into training and testing sets from a methodological perspective. K-nearest neighbors, Decision Trees, Support Vector Regression, and Random Forest are the machine learning models trained and evaluated. The Random Forest model outperforms other models in terms of the accuracy of its predictions of energy consumption. The integration of renewable energy sources with cutting-edge technology to revolutionize energy management practices is the essence of novelty. In conclusion, this investigation underscores the importance of utilizing advanced technologies to promote sustainable energy management, providing practitioners and policymakers with practical insights.

Item Type: Article
Uncontrolled Keywords: Energy management, Big Data, Internet of Things (IoT), Home Energy Management System (HEMS), Machine learning, Renewable energy and Sustainability
Subjects: Q Science > QA Mathematics > QA75 Electronic computers. Computer science
Q Science > QA Mathematics > QA76 Computer software
T Technology > T Technology (General)
Depositing User: Unnamed user with email masilah.mansor@newinti.edu.my
Date Deposited: 12 Nov 2024 05:56
Last Modified: 12 Nov 2024 05:56
URI: http://eprints.intimal.edu.my/id/eprint/2024

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