Multivariable Internet of Things Monitoring for Off-Grid Photovoltaic Operational Diagnostics

Off-grid photovoltaic (PV) systems require low-cost monitoring that captures environmental conditions and energy flows for operational diagnosis. This study evaluates an Internet of Things monitoring framework using 2,500 synchronized observations recorded at 10-minute intervals in May 2025 at a laboratory of Universitas Muhammadiyah Palembang, Indonesia. The objective was to assess whether component-resolved multivariable monitoring could characterize environmental–electrical coupling, identify interpretable operating states, and support operational screening before labeled fault data are available. The monitored system comprised a 1,200-Wp PV array, a 12-V 400-Ah battery bank, two 60-A MPPT controllers, a 3,000-W/VA inverter, and a 350-W water-pump load. Descriptive statistics, Pearson correlation, HC3-robust multiple linear regression, K-means clustering, and power-balance residual analysis were applied. Mean panel temperature, estimated solar irradiance, PV power, charging power, discharging power, and load power were 27.75 °C, 219.77 W/m², 190.14 W, 93.99 W, 98.74 W, and 198.56 W, respectively. PV power correlated strongly with estimated irradiance (r = 0.999) and charging power (r = 0.963), and negatively with discharging power (r = -0.533). The thermal and daytime PV-power models explained 97.7% and 99.5% of within-sample variance, respectively. Residual distributions varied systematically among operating states, supporting their use as internal-consistency and disturbance-sensitive screening indicators rather than definitive fault labels. Because irradiance was estimated from BH1750 illuminance using a published empirical relation without on-site pyranometer calibration, the strong irradiance–PV power correlation indicates internal association rather than validation of absolute irradiance accuracy. The framework therefore provides interpretable operational diagnostics before labeled fault data are available for off-grid PV systems