State of the art of machine learning models in energy systems, a systematic review

Amir Mosavi, Mohsen Salimi, Sina Faizollahzadeh Ardabili, Timon Rabczuk, Shahaboddin Shamshirband, Annamaria R. Varkonyi-Koczy

Research output: Contribution to journalReview articlepeer-review

326 Citations (Scopus)


Machine learning (ML) models have been widely used in the modeling, design and prediction in energy systems. During the past two decades, there has been a dramatic increase in the advancement 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 novel taxonomy of models and applications. Through a novel methodology, ML models are identified and further classified according to the ML modeling technique, energy type, and application area. Furthermore, a comprehensive review of the literature leads to an assessment and performance evaluation of the ML models and their applications, and a discussion of the major challenges and opportunities for prospective research. This paper further concludes that there is an outstanding rise in the accuracy, robustness, precision and generalization ability of the ML models in energy systems using hybrid ML models. Hybridization is reported to be effective in the advancement of prediction models, particularly for renewable energy systems, e.g., solar energy, wind energy, and biofuels. Moreover, the energy demand prediction using hybrid models of ML have highly contributed to the energy efficiency and therefore energy governance and sustainability.

Original languageEnglish
Article number1301
Issue number7
Publication statusPublished - 2019

Bibliographical note

Publisher Copyright:
© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (


  • Artificial neural networks (ANN)
  • Big data
  • Blockchain
  • Decision tree (DT)
  • Deep learning
  • Energy demand
  • Energy informatics
  • Energy systems
  • Ensemble
  • Forecasting
  • Hybrid models
  • Internet of things (IoT)
  • Machine learning
  • Neuro-fuzzy
  • Prediction
  • Remote sensing
  • Renewable energy systems
  • Smart sensors
  • Support vector machines (SVM)
  • Wavelet neural network (WNN)

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment
  • Fuel Technology
  • Energy Engineering and Power Technology
  • Energy (miscellaneous)
  • Control and Optimization
  • Electrical and Electronic Engineering


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