Short-term photovoltaic power forecasting faces significant challenges when clouds move rapidly across solar installations, causing sudden irradiance changes and generation fluctuations. These unpredictable ramps complicate grid operations and increase balancing costs. Traditional forecasting models struggle because they often rely on a single data source, missing the spatial and temporal dynamics of cloud cover.
Researchers have developed an integrated forecasting system that processes multiple data streams simultaneously. The approach combines real-time ground-based weather measurements—including temperature, humidity, and wind speed—with numerical weather prediction models and satellite cloud imagery. By analyzing sequences of cloud images, the algorithm learns to identify cloud texture, boundaries, and movement patterns that precede irradiance changes.
The system uses two specialized neural network components. A temporal encoder processes meteorological time-series data to capture how weather conditions evolve. A spatial encoder analyzes cloud image sequences to detect cloud movement and structural changes. A fusion module then weighs the contribution of each data source based on current weather conditions, giving more emphasis to cloud imagery during overcast periods and meteorological data during clear skies.
To handle rare but operationally critical events—such as sudden cloud clusters causing sharp generation drops—the model incorporates weighted loss functions that penalize forecast errors during disturbances more heavily. Additional constraints based on solar geometry and physical ramp-rate limits improve prediction reliability.
Testing on historical solar farm data demonstrates reduced prediction errors across multiple metrics: root mean square error, mean absolute error, and mean absolute percentage error all declined compared to baseline models. Ramp-tracking accuracy also improved, meaning the algorithm better captures rapid generation changes.
While results are promising, the research focuses primarily on prediction accuracy rather than grid control implications. Operators considering deployment should conduct closed-loop validation studies to confirm benefits in live grid operations and frequency regulation scenarios.
