With population increases and a vital need for energy, energy systems play an important and decisive role in all of the sectors of society. To accelerate the process and improve the methods of responding to this increase in energy demand, the use of models and algorithms based on artificial intelligence has become common and mandatory. In the present study, a comprehensive and detailed study has been conducted on the methods and applications of Machine Learning (ML) and Deep Learning (DL), which are the newest and most practical models based on Artificial Intelligence (AI) for use in energy systems. It should be noted that due to the development of DL algorithms, which are usually more accurate and less error, the use of these algorithms increases the ability of the model to solve complex problems in this field. In this article, we have tried to examine DL algorithms that are very powerful in problem solving but have received less attention in other studies, such as RNN, ANFIS, RBN, DBN, WNN, and so on. This research uses knowledge discovery in research databases to understand ML and DL applications in energy systems’ current status and future. Subsequently, the critical areas and research gaps are identified. In addition, this study covers the most common and efficient applications used in this field; optimization, forecasting, fault detection, and other applications of energy systems are investigated. Attempts have also been made to cover most of the algorithms and their evaluation metrics, including not only algorithms that are more important, but also newer ones that have received less attention.

Electricity plays a vital role in the economic development and welfare of countries. Examining the electricity situation and defining scenarios for developing power plant infrastructure will help countries avoid misguided policies that incur high costs and reduce people’s welfare. In the present research, three scenarios from 2021–2040 have been defined for Iran’s electricity status. The first scenario continues the current trend and forecasts population, electricity consumption, and carbon dioxide emissions from power plants with ARIMA and single and triple exponential smoothing time series algorithms. As part of the second scenario, only non-hydro renewable resources will be used to increase the electricity supply. By ensuring the existence of potential, annual growth patterns have been defined, taking into account the renewable electricity generation achieved by successful nations. The third scenario involves integrating operating gas turbines into combined cycles in exchange for buyback contracts. Economically, this scenario calculates return on investment through an arrangement of various contracts for the seller company and fuel savings for the buyer.

Thermoelectric generators hold immense promise in addressing the ever-increasing global energy demands and environmental concerns. Harnessing waste heat from various sources, such as exhaust gases from internal combustion engines, represents a vital avenue for improving energy efficiency and reducing emissions. In light of this, the present study introduces a comprehensive model for evaluating the performance of thermoelectric generators in heat recovery from diesel engine exhaust, shedding light on the potential of this technology to contribute to sustainable energy solutions. In this study, a model is presented for evaluating a thermoelectric generator's performance in heat recovery. The model is validated using experimental data from the literature. In this setup, 14 thermoelectric modules are placed at both the bottom and top of a rectangular gas channel of a diesel engine to recover heat from the exhaust gas. The hot head is heated by the exhaust gas, while the cold head is cooled by water, maintaining a constant temperature of 293 K. However, the temperature of the hot head varies depending on the engine's speed and load. The study investigates 12 different engine operating modes, including three motor speed modes (1000, 1500, and 2000 rpm) and five motor load modes (0.2, 0.4, 0.6, 0.8, and 1.0 MPa). Numerical analysis is performed concurrently with finite element simulations. The numerical and experimental finite element results are compared, and the findings confirm the consistency of the results.