A Systematic Empirical Evaluation of Machine Learning Algorithm on Energy Prediction

Authors

  • Ibtehaj Ul Haq Commercial Bank International, UAE
  • Waqar Khan Shaheed Zulfikar Ali Bhutto Institute of Science and Technology University, Karachi Pakistan
  • Yusra Khan SZABIST University
  • Syed Sajjad Hussain Rizvi Shaheed Zulfikar Ali Bhutto Institute of Science and Technology University, Karachi Pakistan
  • Shamim Akhtar Majmaah University Saudi Arabia
  • Pardeep Kumar Quaid-e-Awam University of Engineering, Sciences & Technology

DOI:

https://doi.org/10.22555/pjets.v12i1.1065

Keywords:

energy prediction, ML Algorithms, energy optimization, performance compression, ML algorithm

Abstract

The energy crisis has alerted all scientists and researchers in every field. The evident increase in the usage of electronic items causes high electricity consumption. Especially in residential households and high-rise apartments, buildings show high electrical loads. This study is designed to forecast the energy need using AI prediction models and find the most efficient model to predict the future loads of any household considering its surroundings, like weather, air pressure, room temperature and others.  This is a comprehensive comparative study that provides a comprehensive comparison between regression models. The finding of the study is that by using the optimum model, we can achieve the best results in predicting electricity load.

References

Downloads

Published

2024-07-29

How to Cite

A Systematic Empirical Evaluation of Machine Learning Algorithm on Energy Prediction. (2024). Pakistan Journal of Engineering, Technology and Science, 12(1), 117-124. https://doi.org/10.22555/pjets.v12i1.1065

Similar Articles

11-20 of 43

You may also start an advanced similarity search for this article.