Info: Zenodo’s user support line is staffed on regular business days between Dec 23 and Jan 5. Response times may be slightly longer than normal.

Published September 28, 2020 | Version v1
Journal article Open

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

  • 1. Department of Renewable Energies, Niroo Research Institute, Tehran, Iran;
  • 2. Institute of Automation, Kando Kalman Faculty of Electrical Engineering, Obuda University, Budapest, Hungary
  • 3. Biosystem Engineering Department, University of Mohaghegh Ardabili, Ardabil 5619911367, Iran
  • 4. Department of Mechanical Engineering, K.N. Toosi University of Technology, Energy Systems Division, No. 19, Pardis Street, Molla Sadra Ave., Vanak Sq., P.O. Box19395-1999, Tehran, Iran
  • 5. Institute of Structural Mechanics, Bauhaus University Weimar, 99423 Weimar, Germany
  • 6. Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam

Description

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. 
 

Files

2.pdf

Files (1.7 MB)

Name Size Download all
md5:acc6c891e319bb67a02c06bc0aa9bf39
1.7 MB Preview Download