Abstract
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.
Highlights
An energy system is a group of organized elements designed for the purpose of generation, control and/or transformation of energy [1,2]
MLP is an advanced version of ANN for engineering applications and energy systems; it is MLP is an advanced version of ANN for engineering applications and energy systems; it is considered a feed-forward neural network and uses a supervised and back-propagation learning considered a feed-forward neural network and uses a supervised and back-propagation learning method for training purposes [34,35,36]
This is a simple and popular method for the modeling and method for training purposes [34,35,36]. This is a simple and popular method for the modeling and prediction of a process, and, in many cases, it is considered as the control model
Summary
An energy system is a group of organized elements designed for the purpose of generation, control and/or transformation of energy [1,2]. Smart sensors are extensively used in energy production and energy consumption [7,8,9]. Such big data has created a vast number of opportunities and challenges for informed decision-making [10,11]. ML models in energy systems are essential for predictive modeling of production, consumption, and demand analysis due to their accuracy, efficacy and speed [20,21]. Due to the popularity of the field, many review papers have emerged that present insight into present applications and future challenges and opportunities [26]. The contribution of this paper is to present the state of the art of ML models in energy systems and discuss their likely future trends
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