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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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Main Authors: Akhtar, Shamim, Muhamad Zahim, Sujod, Rizvi, Syed Sajjad Hussain
Format: Article
Language:English
Published: MDPI AG 2022
Subjects:
QA76 Computer software
TK Electrical engineering. Electronics Nuclear engineering
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
http://umpir.ump.edu.my/id/eprint/38591/
https://doi.org/10.3390/en15155742
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spelling my.ump.umpir.385912023-09-08T03:59:14Z 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
institution Universiti Malaysia Pahang
building UMP Library
collection Institutional Repository
continent Asia
country Malaysia
content_provider Universiti Malaysia Pahang
content_source UMP Institutional Repository
url_provider http://umpir.ump.edu.my/
language English
topic QA76 Computer software
TK Electrical engineering. Electronics Nuclear engineering
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
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.
format Article
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
title 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_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_sort intelligent data-driven approach for electrical energy load management using machine learning algorithms
publisher MDPI AG
publishDate 2022
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
http://umpir.ump.edu.my/id/eprint/38591/
https://doi.org/10.3390/en15155742
_version_ 1778161090132180992
score 13.211869

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