State of the Art of Machine Learning Models in Energy Systems, a Systematic Review

EasyChair Preprint 4279

Abstract

Machine learning (ML) models have been widely used in diverse applications of energy systems such as design, modeling, complex mappings, system identification, performance prediction, and load forecasting. In particular, the last two decades has seen a dramatic increase in the development and application of various types of ML models for energy systems. This paper presents the state of the art of ML models used in energy systems along with a taxonomy of applications and methods. Through a novel search methodology, ML models are identified and further classified according to the ML modeling technique, energy type, and the application area. Furthermore, a comprehensive review of the literature represents an assessment and performance evaluation of the ML models, their applications and a discussion on the major challenges and opportunities for prospective research. This paper further concludes that there is an outstanding rise in the accuracy, robustness, precision and the generalization ability of the ML models in energy systems using the hybrid and ensemble ML algorithms. ML models are widely used in solar energy and wind energy prediction so that these sustainable energy sources will become more practical and more economic. Energy demand prediction by ML models will also improve our communities’ sustainability.

Keyphrases: ANN., Ensemble., Machine Learning., SVM., deep learning, energy systems., hybrid models., neurofuzzy.

@booklet{EasyChair:4279,
  author    = {Mohsen Salimi and Amir Mosavi and Sina Faizollahzadeh Ardabili and Majid Amidpour and Timon Rabczuk and Shahabodin Shamshirband},
  title     = {State of the Art of Machine Learning Models in Energy Systems, a Systematic Review},
  howpublished = {EasyChair Preprint 4279},
  year      = {EasyChair, 2020}}