Resilient Load Forecasting under Climate Change: Adaptive Conditional Neural Processes for Few-Shot Extreme Load Forecasting

2026-02-04

AI Summary

Extreme weather events induce abrupt shifts in electricity demand and data scarcity, undermining the reliability of conventional forecasting models and threatening grid stability. To address this challenge, this work proposes AdaCNP, a novel approach that introduces an adaptive context reweighting mechanism into Conditional Neural Processes (CNPs) for the first time. By constructing a shared embedding space, AdaCNP dynamically evaluates and reweights the relevance between historical contexts and current conditions, enabling few-shot probabilistic forecasting without fine-tuning. The method substantially enhances generalization under distributional shifts, achieving a 22% reduction in mean squared error compared to the strongest baseline on real-world load data, while also attaining the lowest negative log-likelihood. These results demonstrate AdaCNP’s superior accuracy and reliability in forecasting under extreme events.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Relational Probabilistic Models
• Cognitive Modeling & Cognitive Systems: Adaptive Behavior

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: User modeling for targeted and personalized online advertising
• Web Mining and Content Analysis: Large pretrained models with web data

Abstract

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.

Problem

• extreme load forecasting
• climate change
• distribution shift
• data scarcity
• power system resilience

Innovation

• Adaptive Conditional Neural Processes
• Few-Shot Forecasting
• Extreme Load Prediction
• Probabilistic Forecasting
• Resilient Power Systems