As rooftop solar installations proliferate in residential areas, grid operators face growing challenges in forecasting electricity supply from distributed photovoltaic systems. The variability of cloud cover, time of day, and seasonal factors create unpredictable swings in net load—the difference between household demand and local solar generation. Accurate short-term forecasting is essential for maintaining grid frequency, minimizing reserve margins, and reducing operating costs.
Researchers addressed this challenge by deploying Bayesian neural networks (BNNs) to predict net load for individual and aggregated households in Cyprus. Unlike conventional forecasting methods, BNNs quantify uncertainty in predictions, providing grid operators with probabilistic rather than point estimates. This approach is particularly valuable when managing highly variable renewable sources.
The study evaluated the BNN model using historical consumption and solar generation data from residential customers, testing predictions at 30- and 60-minute intervals. Results demonstrated normalized root mean square errors below 10% across all scenarios—a significant improvement over traditional persistence methods, which assume current conditions will continue unchanged. The BNN model achieved skill scores reaching 49% above the baseline, meaning forecasts were substantially more accurate under diverse weather and operational conditions.
Performance remained robust across varying irradiance levels, indicating the model adapts well to seasonal and diurnal cycles. The BNN's ability to characterize forecast uncertainty is particularly valuable for utility operators who must maintain spinning reserves and manage real-time dispatch.
These results suggest immediate practical applications for integrating machine learning into grid management platforms. As distributed solar capacity expands, utilities require better tools for managing supply-side variability. BNN-based forecasting could enable more efficient commitment of conventional generation, improved demand response coordination, and reduced curtailment of renewable generation. Implementation across multiple distributed resources would enhance overall grid reliability and resilience.
