What question did this study set out to answer?

The research aims to enhance the probabilistic forecasting of photovoltaic power generation with a focus on uncertainty quantification.

January 14, 2026Open Access

Probabilistic Photovoltaic Power Forecasting with Reliable Uncertainty Quantification via Multi-Scale Temporal–Spatial Attention and Conformalized Quantile Regression

Key Points

The research aims to enhance the probabilistic forecasting of photovoltaic power generation with a focus on uncertainty quantification.
Developed a Multi-scale Temporal–Spatial Attention Quantile Regression Network (MTSA-QRN).
Employed Temporal Convolutional Networks (TCN) and Graph Attention Networks (GAT) for data modeling.
Implemented adaptive calibration mechanisms for reliable prediction intervals.
Achieved over 55% improvement in CRPS compared to deep learning baselines.
Attained empirical coverage close to the nominal level (PICP90 = 0.9053) after calibration.
Maintained competitive CRPS values while ensuring reliable uncertainty estimates.

Abstract

Accurate probabilistic forecasting of photovoltaic (PV) power generation is crucial for grid scheduling and renewable energy integration. However, existing approaches often produce prediction intervals with limited calibration accuracy, and the interdependence among meteorological variables is frequently overlooked. This study proposes a probabilistic forecasting framework based on a Multi-scale Temporal–Spatial Attention Quantile Regression Network (MTSA-QRN) and an adaptive calibration mechanism to enhance uncertainty quantification and ensure statistically reliable prediction intervals. The framework employs a dual-pathway architecture: a temporal pathway combining Temporal Convolutional Networks (TCN) and multi-head self-attention to capture hierarchical temporal dependencies, and a spatial pathway based on Graph Attention Networks (GAT) to model nonlinear meteorological correlations. A learnable gated fusion mechanism adaptively integrates temporal–spatial representations, and weather-adaptive modules enhance robustness under diverse atmospheric conditions. Multi-quantile prediction intervals are calibrated using conformalized quantile regression to ensure reliable uncertainty coverage. Experiments on a real-world PV dataset (15 min resolution) demonstrate that the proposed method offers more accurate and sharper uncertainty estimates than competitive benchmarks, supporting risk-aware operational decision-making in power systems. Quantitative evaluation on a real-world 40 MW photovoltaic plant demonstrates that the proposed MTSA-QRN achieves a CRPS of 0.0400 before calibration, representing an improvement of over 55% compared with representative deep learning baselines such as Quantile-GRU, Quantile-LSTM, and Quantile-Transformer. After adaptive calibration, the proposed method attains a reliable empirical coverage close to the nominal level (PICP90 = 0.9053), indicating effective uncertainty calibration. Although the calibrated prediction intervals become wider, the model maintains a competitive CRPS value (0.0453), striking a favorable balance between reliability and probabilistic accuracy. These results demonstrate the effectiveness of the proposed framework for reliable probabilistic photovoltaic power forecasting.

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