Predictive Analytics Based AI Framework for Enhancing Solar Panel Efficiency

Solar photovoltaic (PV) deployments are prone to degradation in performance owing to varying weather conditions, partial shadowing and electrical anomalies. A key to address these issues lies in intelligent monitoring and forecasting methods able to construct meaning out of high volumes of operational data. To ensure accurate power prediction and system reliability this study illustrates predictive analytics framework consisting of gradient-boosted ensemble learning with Long Short-Term Memory (LSTM) networks. The tool captures instantaneous and temporal behavior of PV systems by utilizing real-time Internet of Things (IoT) telemetry from solar irradiance, module temperature, voltage characteristics, and more. We apply a structured feature engineering and preprocessing pipeline to historical SCADA records, sanitizing environmental noise from the data in order to identify signal, coalescing our model learning around those signals. The methodology is assessed using a dataset of 4,213 observations in time-series cross-validation. The experimental analysis shows a high prediction capability of which the coefficient of determination is 0.968 with a root mean square error = 19.4 kW, greatly exceeding the performance of standard statistical forecast method. The findings indicate that the predictive insights provided by the proposed model can assist in detecting abnormal working conditions at an early stage, leading to preventative-based operational initiatives which deliver enhanced energy generation in situations of partial shading. System integration with Maximum Power Point Tracking (MPPT) control helps further reduce generation losses. In a nutshell, the suggested framework stems from data-driven strategies assisting improving operational efficiency and reliability of photovoltaic energy systems thus global implementation of sustainable power generation.