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.

The United Arab Emirates (UAE) is making significant progress in improving its economy by attracting tourists and trade. In the short run, however, economic activity will continue to be more based on oil, natural gas, and related industries. Rising demand for natural gas for power plants and industrial users, such as petrochemicals and steelmakers, has made the UAE a net gas importer, prompting the country to launch multibillion-dollar investments in nuclear and renewable energy. This study addresses the trend of solar energy production and consumption in the UAE. The strengths, weaknesses, opportunities, and threats (SWOT) analysis was performed on the different types of solar energy in the UAE, and some strategies were developed based on it. The SWOT analysis reveals promising strategies for the UAE’s solar energy transition that would reduce fossil fuel demand, mitigate greenhouse gas emissions through solar energy production, and transform the UAE into the carbon market centre of the Gulf Cooperation Council countries.

The clean hydrogen in the prioritized value chain platform could provide energy incentives and reduce environmental impacts. In the current study, strengths, weaknesses, opportunities, and threats (SWOT) analysis has been successfully applied to the clean hydrogen value chain in different sectors to determine Japan’s clean hydrogen value chain’s strengths, weaknesses, opportunities, and threats as a case study. Japan was chosen as a case study since we believe that it is the only pioneer country in that chain with a national strategy, investments, and current projects, which make it unique in this way. The analyses include evaluations of clean energy development, power supply chains, regional energy planning, and renewable energy development, including the internal and external elements that may influence the growth of the hydrogen economy in Japan. The ability of Japan to produce and use large quantities of clean hydrogen at a price that is competitive with fossil fuels is critical to the country’s future success. The implementation of an efficient carbon tax and carbon pricing is also necessary for cost parity. There will be an increasing demand for global policy coordination and inter-industry cooperation. The results obtained from this research will be a suitable model for other countries to be aware of the strengths, weaknesses, opportunities, and threats in this field in order to make proper decisions according to their infrastructures, potentials, economies, and socio-political states in that field.