Photovoltaic (PV) power interval forecasting models are commonly trained on data dominated by non-ramp samples, which may weaken uncertainty characterization during rapid power changes. This study proposes a ramp-aware quantile regression Temporal Fusion Transformer (RQR-TFT) that jointly estimates PV power quantiles and the probability of a future ramp event. Ramp labels are constructed from the normalized power change between adjacent sampling instants. A shared Temporal Fusion Transformer (TFT) encoder extracts temporal representations from historical PV power and meteorological variables, and two output branches perform quantile forecasting and ramp-event identification. Ramp-sample-weighted quantile loss and positive-class-weighted classification loss are jointly optimized to increase the influence of minority ramp samples. The proposed method is evaluated for 4 h ahead forecasting using measurements collected from a 50 MW PV power station during 2019–2020. For the nominal 90% prediction interval, RQR-TFT achieves a ramp-sample prediction interval coverage probability (${\mathrm{PICP}}_R$) of 0.864, an overall prediction interval normalized average width (PINAW) of 0.209, and an overall normalized interval score (NIS) of 0.365. The area under the precision–recall curve for ramp-event identification is 0.906. The results demonstrate improved ramp-sample coverage and overall interval quality, although ramp-sample coverage remains below the nominal level.

State of health (SOH) is a key indicator for safety monitoring, lifetime assessment, and maintenance decisions in lithium-ion battery management systems. However, differences in initial capacity, degradation rate, local capacity variation, and health-indicator evolution often cause models trained on limited source cells to produce biased predictions and unstable trajectories on unseen cells. To address this challenge, we develop the Energy-Aware Warm-Up with Degradation-Consistency Constraints (EWDC) framework for unseen-cell SOH prediction. EWDC couples partial-charge health-indicator extraction, health-indicator graph encoding, cycle-to-cycle degradation-increment prediction, and recursive SOH-trajectory reconstruction. Within-dataset leave-one-battery-out validation was conducted separately on the NASA and CALCE-CS2 datasets. Across the eight held-out cells, EWDC achieved average RMSE, MAE, and $R^2$ values of 1.21%, 0.65%, and 0.975, respectively. Relative to the GNN baseline, EWDC reduced average RMSE and MAE by 32.19% and 38.83%, respectively. Additional limited-source-battery experiments and ablation studies suggest that EWDC improves within-dataset unseen-cell prediction accuracy.