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.

The present article includes studies on the importance of addressing strategic issues in the field of general policies adopted and strategies for the development of renewable energy in Iran. Considering that energy is one of the determining indicators in economic development and the development and optimization of renewable energy can play an effective role in economic growth and protection of environmental conditions, this article tries to introduce the types of renewable energy and general principles. They point to the policies that ultimately determine the priority and necessity of using some of them for the country and explain why and how to use them. The general principles discussed in this article include the advantages, disadvantages, potentials, and, most importantly, comparative comparisons with two neighboring countries. The results of these studies show that despite Iran’s diverse potentials in the field of renewable energy, wind energy has a higher priority than other cases in terms of economic justification and market competition, and domestic production rates. Other renewable energy sources for power generation and grid connection are less of a priority for short- or medium-term investment. However, for non-grid use in the country, they can also be very useful in the short term. In general, Iran can be a hub for renewable energy.

Globally, the construction industry is experiencing an increase in energy demand, which has significant environmental and economic repercussions. To address these issues, it is now possible for buildings, vehicles, and renewable energy sources to collaborate and function as an advanced, integrated, and environmentally favorable system that meets the high energy demands of contemporary buildings. To attain maximum efficiency, however, it is necessary to create reliable energy demand forecasting models. In this research, by introducing the energy model of a neighbourhood with buildings with solar panels and electric vehicles, the final balance of energy production and consumption for each building and the whole neighbourhood as a micro grid is predicted. DesignBuilder is used to model neighbourhood buildings, and K-Nearest neighbor (KNN), Regression Support Vector (SVR), Adaptive Boosting (AdaBoost), and Deep neural networks (DNN) algorithms in machine learning are used to predict the final energy balance. a comparative analysis of the performance of the KNN, SVR, AdaBoost, and DNN algorithms was conducted to determine which algorithm is the most effective in predicting energy balance. Finally, the Root Mean Square Error (RMSE) has been used to validate the prediction models. The results show that the KNN, SVR, AdaBoost, and DNN algorithms had RMSE values of 0.56, 0.92, 0.95, and 0.53, respectively. Among these algorithms, the DNN and KNN algorithms had more accurate results than the other used algorithms and as a result of this research, An accurate forecast of neighbourhood energy balance was made. This study takes a novel approach by developing a model that takes into account an integrated system of houses, solar cells, and electric consumption for each building in a neighborhood, which can help to optimize energy consumption and reduce environmental impact.