An Intelligent Data-Driven Approach for Electrical Energy Load Management Using Machine Learning Algorithms
Data-driven electrical energy efficiency management is the emerging trend in electrical energy forecasting and management. This fusion of data science, artificial intelligence, and electrical energy management has turned out to be the most precise and robust energy management solution. The Smart Ene...
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| Format: | Article |
| Language: | English |
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MDPI AG
2022
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| Online Access: | http://umpir.ump.edu.my/id/eprint/38591/1/An%20Intelligent%20Data-Driven%20Approach%20for%20Electrical%20Energy%20Load%20Management%20Using%20Machine%20Learning%20Algorithms.pdf |
| _version_ | 1825815139113238528 |
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| author | Akhtar, Shamim Muhamad Zahim, Sujod Rizvi, Syed Sajjad Hussain |
| author_facet | Akhtar, Shamim Muhamad Zahim, Sujod Rizvi, Syed Sajjad Hussain |
| author_sort | Akhtar, Shamim |
| collection | UMP |
| description | Data-driven electrical energy efficiency management is the emerging trend in electrical energy forecasting and management. This fusion of data science, artificial intelligence, and electrical energy management has turned out to be the most precise and robust energy management solution. The Smart Energy Informatics Lab (SEIL) of the Indian Institute of Technology (IIT) conducted an experimental study in 2019 to collect massive data on university campus energy consumption. The comprehensive comparative study preparatory to the recommendation of the best candidate out of 24 machine learning algorithms on the SEIL dataset is presented in this work. In this research work, an exhaustive parametric and empirical comparative study is conducted on the SEIL dataset for the recommendation of the optimal machine learning algorithm. The simulation results established the findings that Bagged Trees, Fine Trees, and Medium Trees are, respectively, the best-, second-best-, and third-best-performing algorithms in terms of efficacy. On the contrary, a reverse ranking is observed in terms of efficiency. This is grounded in the fact that Bagged Trees is most effective algorithm for the said application and Medium Trees is the most efficient one. Likewise, Fine Trees has the optimum tradeoff between efficacy and efficiency. |
| first_indexed | 2024-03-06T13:08:58Z |
| format | Article |
| id | UMPir38591 |
| institution | Universiti Malaysia Pahang |
| language | English |
| last_indexed | 2024-03-06T13:08:58Z |
| publishDate | 2022 |
| publisher | MDPI AG |
| record_format | dspace |
| spelling | UMPir385912023-09-08T03:59:14Z http://umpir.ump.edu.my/id/eprint/38591/ An Intelligent Data-Driven Approach for Electrical Energy Load Management Using Machine Learning Algorithms Akhtar, Shamim Muhamad Zahim, Sujod Rizvi, Syed Sajjad Hussain QA76 Computer software TK Electrical engineering. Electronics Nuclear engineering Data-driven electrical energy efficiency management is the emerging trend in electrical energy forecasting and management. This fusion of data science, artificial intelligence, and electrical energy management has turned out to be the most precise and robust energy management solution. The Smart Energy Informatics Lab (SEIL) of the Indian Institute of Technology (IIT) conducted an experimental study in 2019 to collect massive data on university campus energy consumption. The comprehensive comparative study preparatory to the recommendation of the best candidate out of 24 machine learning algorithms on the SEIL dataset is presented in this work. In this research work, an exhaustive parametric and empirical comparative study is conducted on the SEIL dataset for the recommendation of the optimal machine learning algorithm. The simulation results established the findings that Bagged Trees, Fine Trees, and Medium Trees are, respectively, the best-, second-best-, and third-best-performing algorithms in terms of efficacy. On the contrary, a reverse ranking is observed in terms of efficiency. This is grounded in the fact that Bagged Trees is most effective algorithm for the said application and Medium Trees is the most efficient one. Likewise, Fine Trees has the optimum tradeoff between efficacy and efficiency. MDPI AG 2022 Article PeerReviewed pdf en cc_by_4 http://umpir.ump.edu.my/id/eprint/38591/1/An%20Intelligent%20Data-Driven%20Approach%20for%20Electrical%20Energy%20Load%20Management%20Using%20Machine%20Learning%20Algorithms.pdf Akhtar, Shamim and Muhamad Zahim, Sujod and Rizvi, Syed Sajjad Hussain (2022) An Intelligent Data-Driven Approach for Electrical Energy Load Management Using Machine Learning Algorithms. Energies, 15 (15). ISSN 1996-1073. (Published) https://doi.org/10.3390/en15155742 10.3390/en15155742 |
| spellingShingle | QA76 Computer software TK Electrical engineering. Electronics Nuclear engineering Akhtar, Shamim Muhamad Zahim, Sujod Rizvi, Syed Sajjad Hussain An Intelligent Data-Driven Approach for Electrical Energy Load Management Using Machine Learning Algorithms |
| title | An Intelligent Data-Driven Approach for Electrical Energy Load Management Using Machine Learning Algorithms |
| title_full | An Intelligent Data-Driven Approach for Electrical Energy Load Management Using Machine Learning Algorithms |
| title_fullStr | An Intelligent Data-Driven Approach for Electrical Energy Load Management Using Machine Learning Algorithms |
| title_full_unstemmed | An Intelligent Data-Driven Approach for Electrical Energy Load Management Using Machine Learning Algorithms |
| title_short | An Intelligent Data-Driven Approach for Electrical Energy Load Management Using Machine Learning Algorithms |
| title_sort | intelligent data driven approach for electrical energy load management using machine learning algorithms |
| topic | QA76 Computer software TK Electrical engineering. Electronics Nuclear engineering |
| url | http://umpir.ump.edu.my/id/eprint/38591/1/An%20Intelligent%20Data-Driven%20Approach%20for%20Electrical%20Energy%20Load%20Management%20Using%20Machine%20Learning%20Algorithms.pdf |
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