Sistem Pendukung Keputusan Berbasis IoT untuk Monitoring Kinerja Panel Surya dan Pemeliharaan Prediktif - Stephanie Christina Yolanda Pardede; Thea Fitri Astarani; Surya Hardi; Aprima Anugerah Matondang; Regina Sirait (INSOLOGI: Jurnal Sains dan Teknologi, 2026)
Solar energy is a critical component of renewable energy strategies; however, photovoltaic (PV) system performance is frequently degraded by environmental factors such as dust accumulation, shading, and component failures. These issues lead to reduced energy yield and increased operational costs. This research develops an IoT-based decision support system (DSS) for real-time performance monitoring and predictive maintenance scheduling of solar panels. The system integrates sensor data on voltage, current, irradiance, and temperature, transmitted via a wireless network to a cloud-based analytics platform. A fuzzy logic algorithm evaluates panel health and triggers maintenance recommendations when performance deviations exceed pre-defined thresholds. The system was tested under simulated real-world conditions. Results demonstrate that the proposed DSS effectively detects performance degradation due to soiling and module-level faults, achieving a detection accuracy of 94% as indicated by the correlation coefficient between predicted and actual maintenance urgency, enabling timely maintenance interventions. This approach contributes to the field of intelligent renewable energy monitoring by providing a reliable, data-driven tool that reduces unnecessary maintenance costs and maximizes the operational lifespan of PV installations.