Climate Change and Urbanization Impact on Hydropower Plant by Neural Network-Based Decision-Making Methods: Identification of the Most Significant Parameter

The change in climate and rate of urbanization affects and unbalances the equilibrium between the demand and supply of energy resources. Hydropower plants are one of the most affected systems. But not all the operational parameters get influenced by this change. The significance varies between the parameters and indicators. Until now, there are no studies which try to identify the most significant parameters which get mostly affected by the climatic as well as urbanization uncertainties. As a result, the present investigation aims to detect the main indicator which can represent the operational status of hydropower plants under climatic and urbanization impacts. The objective multi criteria decision-making methods followed the implementation of polynomial neural networks to estimate the significance of the indicators. Although the list of indicator is not exhaustive, the said features are mostly consulted before concluding on the operational status of the power plant. According to the results, the most significant parameter was efficiency of generator. In this aspect, it is to be noted that harmonic mean hierarchy process (HMHP) and measuring attractiveness by a categorical-based evaluation technique (MACBETH) methods were used as multi criteria decision-making methods and polynomial neural networks were used to predict the function which will represent the present status of a power plant. In this study, we also used sensitivity analysis.