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

Abstract

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.

Keywords

• distributed generation
• K‐means clustering
• maximum power point tracking
• photovoltic energy
• renewable energy
• sustainable energy