Program development for energy demand estimation andapplication of a region

The electrical energy, whose distribution is controllable but an unstorable energy source itself, is produced in various power plants and transferable to the residential areas via power lines. Due to the increasingly developing technology, industry and increasing population, the demand for electrical energy is increasing as the demand for each energy. One of the most important parameters in determining the development of countries is per capita electricity consumption. The prominence of this matter is also known in our country as it is all over the world. For this reason, the production of electrical energy is tried to be increased. In addition to increasing production, depletion of the resources essential for the mass production and generation of energy due to the damage that can be given to the nature in the course of production are of great importance in economical usage of generated energy with highest efficency as well as in its distribution and delivery. One of the most important ways to ensure energy-saving use of energy is the subject of predicting the demand for energy and there are many ways to do it. Since the electrical energy can not be stored, this energy must be consumed as it is produced. Moreover, the pricing of energy is also influencing the short-term demand forecast value. Therefore, the accuracy rate of the short-term demand forecasting method is very important. A high accuracy rate will provide a rich energy market and also enable power systems to work stably and it will be extremely beneficial for the optimization and reliability of these systems. A high accuracy rate provides a healthy planning for the energy market thanks to the short-term demand forecasting and thereby enables high-quality energy to be used, also saves the user from wasting too much money by extending the systems' lifecycle. In this study focusing on estimation methods, In MATLAB, a short-run demand forecasting method was developed using Artificial Neural Networks.