Clustering of Solar Energy Facilities using a Hybrid Fuzzy c-means (Artificial Intelligence)

Описание: #ArtificialInteligence #SolarEnergy #Clustering #Optimization #Sustainability

Clustering of solar energy facilities using a hybrid fuzzy c-means algorithm initialized by metaheuristics (2018) PDF: https://drive.google.com/file/d/1k1ok...

The research details a new hybrid fuzzy c-means (HFCM) algorithm designed to improve the efficiency of identifying optimal locations for solar energy facilities on unproductive or contaminated land in the United States. Authors examine data from the National Solar Radiation Database (NSRDB) and the EPA's RE-Powering America's Land project to classify areas based on factors like mapped size, distance to transmission lines, and solar irradiance. The core innovation involves initializing the FCM algorithm using metaheuristics such as Differential Evolution (DE), Genetic Algorithm (GA), and Particle Swarm Optimization (PSO), which accelerates the clustering process. The research successfully categorizes locations into two clusters (high potential and low potential) demonstrating the HFCM algorithm's faster convergence and supporting the social, economic, and environmental benefits of revitalizing unproductive land for solar power generation.