Optimizing Solar Energy Harvesting: A K-Means Clustering Approach for Enhanced Efficiency and Viability

Abstract

In recent years, photovoltaic (PV) solar energy has played a crucial role in the global transition toward renewable energy, contributing to 46% of the electric capacity. It has emerged as a primary source; however, optimizing energy utilization and solar panel efficiency to maximize absorbed solar radiation remains a significant challenge. Additionally, it addresses the optimization of solar energy generation and the mitigation of potential overheating issues in dual-axis solar tracking systems. Despite its importance, PV power generation is hindered by uncertainty and intermittency, posing obstacles to achieving a stable and reliable power supply. This research introduces an innovative synthesis method for a typical solar radiation year (TSRY) based on K-means clustering to maximize energy harvest. The K-means algorithm, a fundamental image processing technique, is utilized to classify images into distinct groups. This approach enhances energy generation potential, panel efficiency, and the long-term sustainability of solar energy systems compared to conventional methods.