In this century, with increasing society’s population and Gross Domestic Product (GDP) trend, energy demand is increased in the countries of the whole world. Nowadays, the use of different Renewable Energy Sources (RESs) in the network has become commonplace and, of course, has been challenged. In this way, forecasting energy demand plays a key role in the development of different parts of a country. In this study, firstly a prediction of consumption and fluctuations in the sources of energy is made, and secondly, regarding different parts of the industry, agriculture, and households, two different scenarios have been analyzed to provide this demand in the future. An Artificial Neural Network (ANN) method has been used to predict energy consumption level, and also the two factors of the increase in population and GDP have been considered. Prediction of population increase rate, with respect to its statistical complexities, is derived from a literature review of other references; the GDP prediction is derived with a conventional method of the Grey method. Then, with the prediction of the aforementioned factors, energy consumption is predicted by a metaheuristic algorithm. Afterwards, scenarios related to the energy consumption are predicted and priorities are given, such as environmental impacts, in order to provide the predicted consumption level. Scenarios will considerably show that the supply and demand should be managed by fossil fuel energy production replaced with RESs in the supply side, and providing products with higher energy efficiency in the demand side.