Predicting future values using historical data patterns, statistical models, and machine learning algorithms
Spatiotemporal forecasting of regional photovoltaic production from Meteosat image sequences using a ConvLSTM model trained and evaluated against measured production and a persistence baseline.
Scientific framework for evaluating solar forecasts beyond average error metrics, using Temporal Distortion Mix and a ramp metric to assess timing errors and rapid irradiance variations.
Fusion of ground-based sky-camera and geostationary satellite imagery to reconstruct cloud geometry and forecast solar irradiance and cloud shadows from 10 minutes to several hours ahead.
End-to-end production platform using machine learning for site-specific solar-generation and facility-load forecasts, refreshed every 15 minutes over a three-day horizon and integrated with downstream energy-management workflows.
PyTorch