A new maximum power point tracking (MPPT) methodology combines artificial neural networks with real-time meteorological data acquisition to optimize solar panel performance under variable environmental conditions. Rather than relying on static algorithms, the system employs incremental learning—continuously retraining its neural network model based on hourly irradiance and temperature measurements obtained from cloud-based meteorological services.
The research team integrated a Raspberry Pi microcontroller with the NASA POWER API to autonomously gather weather data and feed it into their ANN-based tracking algorithm. This architecture enables the system to adapt dynamically as clouds, seasonal changes, and temperature fluctuations affect solar radiation intensity at the installation site.
Simulation testing across irradiance levels from 200 to 1000 W/m² revealed exceptional performance metrics: the system achieved 99.52% PV conversion efficiency and 98.50% load-side efficiency. These results substantially exceed conventional MPPT techniques and competing intelligent approaches documented in peer-reviewed literature. The regression accuracy for maximum voltage prediction reached an R² value of 0.9987 with minimal mean squared error of 0.0024.
Beyond raw efficiency gains, the adaptive approach demonstrated rapid response times and stable power tracking across diverse operating scenarios—critical for real-world deployment where conditions change continuously throughout each day. The low computational overhead of the Raspberry Pi implementation suggests scalability for residential and small commercial solar arrays without requiring expensive industrial control equipment.
This work bridges academic machine learning advances and practical solar engineering by demonstrating that accessible hardware combined with cloud-based meteorological data can substantially improve distributed solar generation. Future applications may extend to mixed renewable portfolios where coordinated MPPT systems across multiple sites could optimize grid-connected generation profiles.
