AN INNOVATIVE PSO BASED EMPIRICAL MODEL FOR ESTIMATING CDM COSTS IN HYDROELECTRIC POWER PLANTS

This study aims to provide a more realistic and sustainable perspective for estimating the investment costs of hydroelectric power plants within the framework of the Clean Development Mechanism (CDM). For the first time in the literature, the CDM parameter has been directly integrated into an empirical equation model, and the model parameters have been optimized using the Particle Swarm Optimization (PSO) algorithm. The proposed model was tested with technical and economic data from various hydroelectric power plants in India, achieving high accuracy in cost estimation by considering both installed capacity and the level of CDM implementation. The results demonstrate that the proposed model operates with lower error rates compared to conventional methods and enables a quantitative analysis of the impact of sustainability-oriented policy instruments on energy costs. In this study, 30 hydroelectric power plants in India were analyzed, and highly accurate cost estimations were obtained using the proposed equation model in conjunction with the optimization algorithm, resulting in a total error of 4.49 and an average error of 0.14. In this respect, the study offers a significant methodological contribution to the integration of sustainability criteria into investment planning and policy development processes in the energy sector.