Due to the increasing population, a great increase is observed in the number of different centers such as art, entertainment, and industry. The number of such centers is increasing day by day. These areas are naturally the centers where energy is needed at a high rate. Energy consumption data in the specified areas are increasing day by day. At this point, it has become a difficult problem to meet energy needs in all areas where people live and need energy, in addition to the mentioned centers. To eliminate this difficult problem, it has become a necessity to both meet the energy consumption and ensure the effective use of energy. It is observed that there is an increase in artificial intelligence supported housing systems consisting of electronic devices in order to minimize energy consumption in shelters and residences. Taking into account the increase in environmental factors such as global warming, greenhouse gas emissions, carbon dioxide, chemical solvents, and radiation, studies on the efficient use of energy should be increased. In line with the stated objectives and purposes, the data of the United States regional communications organization PJM Interconnection LLC (PJM) named Dominion Virginia Power (DOM) have been used. This dataset shows the hourly data consumption in Mega Watts of the Asian region. On this dataset, the energy estimation results of the recently popular XGBoost, LSTM algorithms, classical linear regression and RANSAC algorithms were compared.