A Multi-Source Relational Data Framework for Very Short-Term PV Power Forecasting Using Wavelet-Coupled Deep Learning

Accurate photovoltaic power forecasting is essential for the reliable integration of solar energy into the electrical grid. This work presents a high-resolution dataset and acquisition framework that integrates electrical measurements, environmental variables, and solar position data into a unified relational database, suitable for PV power prediction across all temporal horizons. Using this dataset, we focus on very-short-term forecasting and propose a comprehensive forecasting framework that combines wavelet-based feature extraction with advanced deep learning techniques. The framework is evaluated across forecasting horizons from 5 to 30 min, achieving nMAE values between 0.73% and 4.64%, nRMSE between 1.65% and 7.98%, and PICP ranging from 62.4% to 74.7%. Robustness is assessed by simulating realistic cloud-induced perturbations in the input data. A hybrid approach that combines the deep learning model with a gradient boosting regressor to correct residual errors reduces the overall nMAE from 4.72% to 3.89% and nRMSE from 9.52% to 6.83%, effectively mitigating large errors caused by abrupt power fluctuations. These results demonstrate the framework’s ability to provide accurate and reliable probabilistic forecasts under both standard and perturbed conditions, offering a solid foundation for future PV prediction research and practical applications.