Extreme weather can substantially change electricity consumption behavior, causing load curves to exhibit sharp spikes and pronounced volatility. If forecasts are inaccurate during those periods, power systems are more likely to face supply shortfalls or localized overloads, forcing emergency actions such as load shedding and increasing the risk of service disruptions and public-safety impacts. This problem is inherently difficult because extreme events can trigger abrupt regime shifts in load patterns, while relevant extreme samples are rare and irregular, making reliable learning and calibration challenging. We propose AdaCNP, a probabilistic forecasting model for data-scarce condition. AdaCNP learns similarity in a shared embedding space. For each target data, it evaluates how relevant each historical context segment is to the current condition and reweights the context information accordingly. This design highlights the most informative historical evidence even when extreme samples are rare. It enables few-shot adaptation to previously unseen extreme patterns. AdaCNP also produces predictive distributions for risk-aware decision-making without expensive fine-tuning on the target domain. We evaluate AdaCNP on real-world power-system load data and compare it against a range of representative baselines. The results show that AdaCNP is more robust during extreme periods, reducing the mean squared error by 22\% relative to the strongest baseline while achieving the lowest negative log-likelihood, indicating more reliable probabilistic outputs. These findings suggest that AdaCNP can effectively mitigate the combined impact of abrupt distribution shifts and scarce extreme samples, providing a more trustworthy forecasting for resilient power system operation under extreme events.

翻译：极端天气可显著改变电力消费行为，导致负荷曲线呈现尖峰特征与剧烈波动。若在此期间预测失准，电力系统更易面临供电短缺或局部过载，迫使采取切负荷等紧急措施，增加服务中断风险与公共安全影响。此问题本质困难在于：极端事件可能引发负荷模式的突变性状态转移，而相关极端样本稀缺且分布不规则，导致可靠学习与校准极具挑战。本文提出AdaCNP——一种面向数据稀缺条件的概率预测模型。AdaCNP在共享嵌入空间中学习相似性度量，针对每个目标数据评估各历史上下文片段与当前条件的关联程度，并据此对上下文信息进行重加权。该设计能在极端样本稀缺时突出最具信息量的历史证据，实现对未见极端模式的少样本自适应。AdaCNP还可生成预测分布以支持风险感知决策，无需在目标域进行昂贵的微调。我们在真实电力系统负荷数据上评估AdaCNP，并与一系列代表性基线方法比较。结果表明，AdaCNP在极端时期具有更强鲁棒性：相较于最强基线，其均方误差降低22%，同时取得最低负对数似然值，表明其概率输出更为可靠。这些发现证明AdaCNP能有效缓解分布突变与极端样本稀缺的复合影响，为极端事件下的弹性电力系统运行提供更可信的预测支撑。