Chengjun Zhang

Identifying distributed photovoltaic generation, battery energy storage, and load-dominant behaviour remains challenging when transformer-area measurements are sparse, operating patterns overlap, and reference labels are incomplete. We develop a knowledge-guided multimodal learning framework that combines raw electrical sequences with temporal, statistical, frequency-domain, and contextual descriptors. A knowledge-guided label library reconciles archived operating records, expert rules, and clustering-based screening, while transfer learning and WGAN-based augmentation are used to improve learning under limited and imbalanced data. The proposed framework jointly encodes raw electrical sequences, engineered descriptors, and contextual information and integrates their complementary representations through attention-based multimodal fusion. On a held-out real-only test set with independently verified labels, the proposed framework achieved 93.8% accuracy and a macro-F1 of 93.8%, while retaining 90.4% accuracy when 30% of the inputs were randomly masked. These results support further evaluation of knowledge-guided multimodal learning for transformer-area resource identification, although broader cross-region and cross-utility validation is required before operational deployment.

Quantum reinforcement learning (QRL) is often evaluated under idealized, noiseless assumptions, yet realistic quantum devices inevitably introduce noise that can severely degrade performance. This paper improves the robustness of quantum deep Q-learning (QDQN) by redesigning the variational quantum circuit (VQC) used in its value-function approximator. Motivated by recent advances in quantum convolutional neural networks (QCNNs), we construct four QCNN-inspired VQC variants (Models A–D) by combining representative QCNN two-qubit building blocks with an explicit fully connected (all-to-all) layer. Using a 10-fold evaluation protocol at a fixed noise level p = 0.005, Model D achieves the best robustness, reducing the mean number of episodes required to reach a target reward from 1981 (baseline) to 1243. Under a stricter success criterion, Model D also doubles the empirically observed noise-tolerance boundary from 0.002 to 0.004. These results indicate that carefully chosen QCNN-style circuit components and connectivity can significantly improve the noise robustness of QDQN-like QRL agents.

This paper proposes a Dynamic Flow Spatio-Temporal Generative Adversarial Network (DFST-GAN) model for high-quality precipitation nowcasting. Current spatio-temporal prediction models struggle with two key limitations: the inability to adaptively capture complex motion patterns and the tendency to generate blurry predictions over time. To address these challenges, DFST-GAN integrates a dynamic flow feature extraction mechanism with a novel specialized meteorological discriminator, enabling adaptive modeling of complex precipitation system trajectories and generating sharper, physically consistent predictions. We evaluate our approach on the HKO-7 dataset using metrics including CSI, HSS, POD, FAR and ETS. Experimental results demonstrate that DFST-GAN consistently outperforms existing methods across all evaluation metrics, with particularly notable improvements for moderate to heavy rainfall events (dBZ ≥ 50), showing a 18.8% relative improvement in CSI compared to PredRNN-V2. The ablation studies confirm that each component makes a meaningful contribution to overall performance, validating the potential of our approach for operational precipitation nowcasting applications.

The ability to capture long-distance dependencies is critical for improving the prediction accuracy of spatiotemporal prediction models. Traditional ConvLSTM models face inherent limitations in this regard, along with the challenge of information decay, which negatively impacts prediction performance. To address these issues, this paper proposes a QSA-QConvLSTM model, which integrates quantum convolution circuits and quantum self-attention mechanisms. The quantum self-attention mechanism maps query, key, and value vectors using variational quantum circuits, effectively enhancing the ability to model long-distance dependencies in spatiotemporal data. Additionally, the use of quantum convolution circuits improves the extraction of spatial features. Experiments on the Moving MNIST dataset demonstrate the superiority of the QSA-QConvLSTM model over existing models, including ConvLSTM, TrajGRU, PredRNN, and PredRNN v2, with MSE and SSIM scores of 44.3 and 0.906, respectively. Ablation studies further verify the effectiveness and necessity of the quantum convolution circuits and quantum self-attention modules, providing an efficient and accurate approach to quantized modeling for spatiotemporal prediction tasks.

This study advances the precision and efficiency of precipitation nowcasting, particularly under extreme weather conditions. Traditional forecasting methods struggle with precision, spatial feature generalization, and recognizing long-range spatial correlations, challenges that intensify during extreme weather events. The Enhanced Temporal Correlation Jump Prediction Network (ETCJ-PredNet) introduces a novel attention mechanism that optimally leverages spatiotemporal data correlations. This model scrutinizes and encodes information from previous frames, enhancing predictions of high-intensity radar echoes. Additionally, ETCJ-PredNet addresses the issue of gradient vanishing through an innovative jump connection strategy. Comparative experiments on the Moving Modified National Institute of Standards and Technology (Moving-MNIST) and Hong Kong Observatory Dataset Number 7 (HKO-7) validate that ETCJ-PredNet outperforms existing models, particularly under extreme precipitation conditions. Detailed evaluations using Critical Success Index (CSI), Heidke Skill Score (HSS), Probability of Detection (POD), and False Alarm Ratio (FAR) across various rainfall intensities further underscore its superior predictive capabilities, especially as rainfall intensity exceeds 30 dbz,40 dbz, and 50 dbz. These results confirm ETCJ-PredNet’s robustness and utility in real-time extreme weather forecasting.