Machine learning for smart photovoltaic applications- forecasting to welcome the solar era

Abstract

The inherent uncertainty in photovoltaic (PV) power generation remains a significant challenge to the seamless integration of solar energy into modern power systems. This study addresses this issue by employing advanced machine learning (ML) techniques, with a particular focus on Long Short-Term Memory (LSTM) networks—a class of recurrent neural networks (RNNs) to improve the forecasting accuracy of solar power output. The methodology combines deep learning models with Maximum Power Point (MPP) tracking to enhance both predictive performance and operational efficiency of PV systems. The research integrates time-series data collected from real-world PV installations, capturing key variables such as solar irradiance, temperature, voltage, and current. The LSTM architecture is trained to model the temporal dependencies inherent in these sequences, allowing for accurate forecasting of solar power production. An ensemble LSTM approach is implemented to further enhance the robustness of predictions and reduce mean squared error (MSE). Moreover, the integration of MPP data enables real-time adaptation to changing environmental conditions, thereby improving energy capture efficiency and enabling early detection of system faults. Complementary to the LSTM framework, traditional time-series models such as SARIMA and ARIMA are also applied to analyze the temporal variation in solar power production. These statistical models provide valuable baseline comparisons and offer additional insights into the volatility of PV output, which is known to cause operational issues such as frequency instability, dispatch challenges, and voltage/current surges within the grid. The research demonstrates a hybrid methodology that leverages deep learning and statistical modeling, coupled with MPP analysis, to enhance the reliability, efficiency, and fault tolerance of solar energy systems paving the way for more stable and scalable integration of PV power into the energy mix.