An early cost estimation model for hydroelectric power plant projects using neural networks and multiple regression analysis

Energy is increasingly becoming more important in today's world, whereas energy sources are drastically decreasing. One of the most valuable energy sources is hydro energy. Because of limited energy sources and excessive energy usage, cost of energy is rising. Among the electricity generation units, hydroelectric power plants are very important, since they are renewable energy sources and they have no fuel cost. To decide whether a hydroelectric power plant investment is feasible or not, project cost and amount of electricity generation of the investment should be precisely estimated. In this paper, fifty four hydroelectric power plant projects are analysed by using multiple regression and artificial neural network tools. As a result, two cost estimation models have been developed to estimate the hydroelectric power plant project cost in early stages of the project.