High-resolution weather-guided surrogate modeling for data-efficient cross-location building energy prediction

2026-03-11

AI Summary

Traditional building energy simulation is computationally expensive, and existing surrogate models struggle to generalize across regions, particularly underperforming in data-scarce new locations. This work proposes a week-scale weather-guided surrogate modeling approach that captures short-term weather–energy consumption dynamics common across multiple sites, enabling the construction of a universal model capable of cross-climate-zone transfer without requiring multi-site training. By integrating high-resolution weather feature embeddings, cross-regional pattern alignment, and a data-efficient deep learning architecture, the method achieves near-lossless cross-location prediction within a single climate zone and only modest performance degradation across distinct climate zones. This significantly enhances the model’s scalability, reusability, and generalization capability.

Technology Category

• Machine Learning: Deep Neural Architectures and Foundation Models
• Planning, Routing, and Scheduling: Planning with Language Models
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Search and Retrieval-Augmented AI: Multilingual and cross-lingual Web search
• User Modeling, Personalization and Recommendation: User modeling and simulation for interactive and conversational systems

Abstract

Building design optimization often depends on physics-based simulation tools such as EnergyPlus, which, although accurate, are computationally expensive and slow. Surrogate models provide a faster alternative, yet most are location-specific, and even weather-informed variants require simulations from many sites to generalize to unseen locations. This limitation arises because existing methods do not fully exploit the short-term weather-driven energy patterns shared across regions, restricting their scalability and reusability. This study introduces a high-resolution (weekly) weather-informed surrogate modeling approach that enhances model reusability across locations. By capturing recurring short-term weather-energy demand patterns common to multiple regions, the proposed method produces a generalized surrogate that performs well beyond the training location. Unlike previous weather-informed approaches, it does not require extensive simulations from multiple sites to achieve strong generalization. Experimental results show that when trained on a single location, the model maintains high predictive accuracy for other sites within the same climate zone, with no noticeable performance loss, and exhibits only minimal degradation when applied across different climate zones. These findings demonstrate the potential of climate-informed generalization for developing scalable and reusable surrogate models, supporting more sustainable and optimized building design practices.

Problem

• surrogate modeling
• building energy prediction
• cross-location generalization
• weather-driven patterns
• data efficiency

Innovation

• weather-guided surrogate modeling
• high-resolution temporal modeling
• cross-location generalization
• building energy prediction
• data-efficient simulation