Integration of Edge Computing and Wireless Sensors for Energy Efficiency Monitoring in Solar Panels

Abstract

Increased demand for renewable energy has driven the development of efficient monitoring systems to optimize solar panel performance. This study aims to implement and evaluate the integration of edge computing technology with wireless sensor networks (WSN) in real-time solar panel energy efficiency monitoring systems. This approach is designed to overcome the limitations of conventional monitoring systems that still rely on centralized computing and exhibit high latency in data collection. The research method includes designing an edge computing-based system architecture, installing wireless sensors to measure key parameters (voltage, current, light intensity, and temperature), and applying energy efficiency algorithms at the edge to process data locally. The data is then sent to the cloud for in-depth analysis and visualization of system performance. Testing was conducted by comparing data transmission efficiency, response time, and measurement accuracy between edge-based and conventional systems. The results of the study show that the integration of edge computing and wireless sensors can increase monitoring efficiency by up to 28.4%, reduce system latency by 35.7%, and increase data accuracy by 12.6% compared to conventional systems that are entirely cloud-based. In addition, bandwidth consumption is significantly reduced because the computing process is done on the edge.