Geospatial Clustering of Wind/Solar Potential: Identifying High-Yield Areas in CearÁ, Northeast Brazil
Abstract
This article addresses the identification of municipalities in the state of Ceará with the greatest potential for wind and photovoltaic power generation, using a data clustering method applied to climatic and geographic variables. For this purpose, variables such as altitude, wind speed, solar irradiation, and average temperature were considered. Based on these criteria, the cities were distributed into distinct clusters using the K-means algorithm. The results revealed the existence of distinct groups of municipalities with specific characteristics for each type of power generation. To classify the groups most suitable for power generation, a scoring methodology based on technical criteria was employed. Each group received specific scores according to its influence on the type of generation, in order to identify the favorable groups for wind and photovoltaic generation. The total score for each cluster was obtained by summing the points of the variables for each generation type, allowing for a classification of the most promising groups. Based on the results, it was found that Group 3 proved to be favorable for wind power generation due to its high temperatures and wind speed. On the other hand, Group 1 was identified as more suitable for photovoltaic generation due to its higher altitude and milder temperatures.
Keywords
Renewable Energy, Wind Energy, Photovoltaic Energy, K-means algorithm, Clusterization