Version 1
: Received: 10 March 2019 / Approved: 11 March 2019 / Online: 11 March 2019 (10:09:33 CET)
How to cite:
Mosavi, A.; Bahmani, A. Energy Consumption Prediction Using Machine Learning; A Review. Preprints2019, 2019030131. https://doi.org/10.20944/preprints201903.0131.v1
Mosavi, A.; Bahmani, A. Energy Consumption Prediction Using Machine Learning; A Review. Preprints 2019, 2019030131. https://doi.org/10.20944/preprints201903.0131.v1
Mosavi, A.; Bahmani, A. Energy Consumption Prediction Using Machine Learning; A Review. Preprints2019, 2019030131. https://doi.org/10.20944/preprints201903.0131.v1
APA Style
Mosavi, A., & Bahmani, A. (2019). Energy Consumption Prediction Using Machine Learning; A Review. Preprints. https://doi.org/10.20944/preprints201903.0131.v1
Chicago/Turabian Style
Mosavi, A. and Abdullah Bahmani. 2019 "Energy Consumption Prediction Using Machine Learning; A Review" Preprints. https://doi.org/10.20944/preprints201903.0131.v1
Abstract
Machine learning (ML) methods has recently contributed very well in the advancement of the prediction models used for energy consumption. Such models highly improve the accuracy, robustness, and precision and the generalization ability of the conventional time series forecasting tools. This article reviews the state of the art of machine learning models used in the general application of energy consumption. Through a novel search and taxonomy the most relevant literature in the field are classified according to the ML modeling technique, energy type, perdition type, and the application area. A comprehensive review of the literature identifies the major ML methods, their application and a discussion on the evaluation of their effectiveness in energy consumption prediction. This paper further makes a conclusion on the trend and the effectiveness of the ML models. As the result, this research reports an outstanding rise in the accuracy and an ever increasing performance of the prediction technologies using the novel hybrid and ensemble prediction models.
Keywords
energy consumption; prediction; machine learning models; deep learning models; 21 artificial intelligence (AI); computational intelligence (CI); forecasting; soft computing (SC)
Subject
Engineering, Energy and Fuel Technology
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
