Predicting the Performance of Solar-Powered CCTV Systems: A Comparative of Rural and Urban Performance

Source abstract

Reliable surveillance in areas with limited grid access increasingly depends on solar-powered CCTV systems. However, the performance of such systems varies significantly across rural and urban environments due to differences in irradiance patterns, shading, weather variability, load characteristics, and installation constraints. This study proposes a data-driven framework that employs neural networks to predict the operational performance of solar-powered CCTV installations, with the goal of estimating battery state-of-charge, available runtime, and the likelihood of system downtime. Environmental factors (solar irradiance, temperature, cloud cover), system parameters (PV capacity, battery specifications, inverter efficiency), and operational variables (camera load, motion events, communication activity) were collected from representative rural and urban sites and used to train both regression and classification models. Several neural network architectures including multilayer perceptrons, long short-term memory (LSTM) networks, and temporal convolutional networks (TCNs)—were evaluated using time-series cross-validation. Results show that LSTM-based models outperform baseline machine learning approaches, achieving high accuracy in multi-hour energy forecasting and significantly reducing false downtime predictions. Comparative analysis reveals that rural systems generally exhibit more stable generation patterns, while urban systems suffer prediction drift due to intermittent shading and load variability. The findings demonstrate the feasibility of using neural networks for proactive energy management, system sizing optimization, and predictive maintenance of solar-powered CCTV deployments across diverse environments.