Big Data and Machine Learning-Based Iot Models for Sustainable Energy Prediction
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...
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| Main Authors: | , |
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| Format: | Article |
| Language: | English English |
| Published: |
INTI International University
2024
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| Subjects: | |
| Online Access: | http://eprints.intimal.edu.my/2024/1/joit2024_20.pdf http://eprints.intimal.edu.my/2024/2/566 http://eprints.intimal.edu.my/2024/ http://ipublishing.intimal.edu.my/joint.html |
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| Summary: | 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. |
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