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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| Format: | Article |
| Language: | English English |
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INTI International University
2024
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| Online Access: | http://eprints.intimal.edu.my/2024/ http://eprints.intimal.edu.my/2024/1/joit2024_20.pdf http://eprints.intimal.edu.my/2024/2/566 |
| _version_ | 1848766899967492096 |
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| author | Sravani, Parvathareddy Vinitha, Kanakambaran |
| author_facet | Sravani, Parvathareddy Vinitha, Kanakambaran |
| author_sort | Sravani, Parvathareddy |
| building | INTI Institutional Repository |
| collection | Online Access |
| description | 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. |
| first_indexed | 2025-11-14T11:58:29Z |
| format | Article |
| id | intimal-2024 |
| institution | INTI International University |
| institution_category | Local University |
| language | English English |
| last_indexed | 2025-11-14T11:58:29Z |
| publishDate | 2024 |
| publisher | INTI International University |
| recordtype | eprints |
| repository_type | Digital Repository |
| spelling | intimal-20242024-11-12T05:56:57Z http://eprints.intimal.edu.my/2024/ Big Data and Machine Learning-Based Iot Models for Sustainable Energy Prediction Sravani, Parvathareddy Vinitha, Kanakambaran QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) 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. INTI International University 2024-11 Article PeerReviewed text en cc_by_4 http://eprints.intimal.edu.my/2024/1/joit2024_20.pdf text en cc_by_4 http://eprints.intimal.edu.my/2024/2/566 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 http://ipublishing.intimal.edu.my/joint.html |
| spellingShingle | QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) Sravani, Parvathareddy Vinitha, Kanakambaran Big Data and Machine Learning-Based Iot Models for Sustainable Energy Prediction |
| title | Big Data and Machine Learning-Based Iot Models for Sustainable Energy Prediction |
| title_full | Big Data and Machine Learning-Based Iot Models for Sustainable Energy Prediction |
| title_fullStr | Big Data and Machine Learning-Based Iot Models for Sustainable Energy Prediction |
| title_full_unstemmed | Big Data and Machine Learning-Based Iot Models for Sustainable Energy Prediction |
| title_short | Big Data and Machine Learning-Based Iot Models for Sustainable Energy Prediction |
| title_sort | big data and machine learning-based iot models for sustainable energy prediction |
| topic | QA75 Electronic computers. Computer science QA76 Computer software T Technology (General) |
| url | http://eprints.intimal.edu.my/2024/ http://eprints.intimal.edu.my/2024/ http://eprints.intimal.edu.my/2024/1/joit2024_20.pdf http://eprints.intimal.edu.my/2024/2/566 |