Preprint Review Version 1 This version is not peer-reviewed

Demand Prediction with Machine Learning Models; State of the Art and a Systematic Review of Advances

Version 1 : Received: 12 May 2019 / Approved: 14 May 2019 / Online: 14 May 2019 (14:00:40 CEST)

How to cite: Mosavi, A.; Faizollahzadeh Ardabili, S.; Shamshirband , S.. Demand Prediction with Machine Learning Models; State of the Art and a Systematic Review of Advances. Preprints 2019, 2019050175 (doi: 10.20944/preprints201905.0175.v1). Mosavi, A.; Faizollahzadeh Ardabili, S.; Shamshirband , S.. Demand Prediction with Machine Learning Models; State of the Art and a Systematic Review of Advances. Preprints 2019, 2019050175 (doi: 10.20944/preprints201905.0175.v1).

Abstract

Electricity demand prediction is vital for energy production management and proper exploitation of the present resources. Recently, several novel machine learning (ML) models have been employed for electricity demand prediction to estimate the future prospects of the energy requirements. The main objective of this study is to review the various ML models applied for electricity demand prediction. Through a novel search and taxonomy, the most relevant original research articles in the field are identified and further classified according to the ML modeling technique, perdition type, and the application area. A comprehensive review of the literature identifies the major ML models, their applications and a discussion on the evaluation of their performance. This paper further makes a discussion on the trend and the performance of the ML models. As the result, this research reports an outstanding rise in the accuracy, robustness, precision and the generalization ability of the prediction models using the hybrid and ensemble ML algorithms.

Subject Areas

demand prediction, energy systems; machine learning; artificial neural network (ANN); support vector machines (SVM); neuro-fuzzy; ANFIS; wavelet neural network (WNN); big data; decision tree (DT); ensemble learning; hybrid models; data science; deep learning; renewable energies; energy informatics; prediction; forecasting; energy demand

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