Solar Energy Production Forecasting With Hyperparameter-Optimized Time-Series Dense Encoder

Abstract

Photovoltaic power production forecasting is essential for grid stability, market operation, and large-scale integration of renewable energy, yet it remains challenging due to the nonlinear and weather-driven nature of solar generation. This work introduces a fully automatic photovoltaic forecasting framework that combines the time-series dense encoder architecture with a genetic hyperparameter space explorer based on the non-dominated sorting genetic algorithm III. The proposed framework employs a computationally efficient encoder-decoder architecture that leverages historical photovoltaic production, sun-position features, and meteorological covariates, while evolutionary search adapts architectural and training configurations to each forecasting horizon. The approach is evaluated on a six-year real-world dataset from a solar farm in Colombia, covering short-, medium-, and long-term horizons. Across all experiments, the proposed method achieves strong results compared to statistical, machine learning, and deep learning baselines. In addition, a rigorous statistical evaluation provides detailed insight into the reliability and robustness of the forecasts. A complementary computational analysis quantifies training and inference complexity, showing that the proposed approach is computationally efficient and scalable. These results show that the proposed framework provides a promising approach for solar power forecasting, with potential to support energy planning, resource allocation, and the broader integration of renewable energy technologies.