High-resolution weather-guided surrogate modeling for data-efficient cross-location building energy prediction
arXiv:2603.11121v1 Announce Type: new 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 simu
arXiv:2603.11121v1 Announce Type: new 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.
Executive Summary
This article presents a novel high-resolution weather-guided surrogate modeling approach for cross-location building energy prediction. The method leverages recurring short-term weather-energy demand patterns shared across regions to produce a generalized surrogate model that performs well beyond the training location. Experimental results demonstrate the model's potential for scalable and reusable surrogate models, supporting sustainable and optimized building design practices. The proposed approach does not require extensive simulations from multiple sites, overcoming a significant limitation of previous weather-informed methods. This breakthrough has significant implications for building design optimization, energy efficiency, and climate change mitigation.
Key Points
- ▸ Introduces a high-resolution weather-guided surrogate modeling approach for cross-location building energy prediction
- ▸ Leverages recurring short-term weather-energy demand patterns shared across regions
- ▸ Produces a generalized surrogate model that performs well beyond the training location without requiring extensive simulations from multiple sites
Merits
Strength in Generalizability
The proposed method achieves strong generalization across locations without requiring extensive simulations from multiple sites, making it a significant improvement over previous weather-informed approaches.
Scalability and Reusability
The model's ability to perform well beyond the training location and maintain high predictive accuracy across locations enables scalable and reusable surrogate models, supporting sustainable and optimized building design practices.
Demerits
Limited Climate Zone Consideration
The model exhibits minimal degradation when applied across different climate zones, but its performance may be affected by more significant climatic differences.
Potential for Overfitting
The model's reliance on recurring short-term weather-energy demand patterns may lead to overfitting if not properly regularized or validated.
Expert Commentary
This article presents a significant breakthrough in building energy prediction, leveraging high-resolution weather data to create a generalized surrogate model. The proposed method's ability to perform well beyond the training location and maintain high predictive accuracy across locations makes it an attractive solution for building design optimization and energy efficiency. While the model's performance is affected by climate zone differences, its potential for scalability and reusability is substantial. As the field of building energy prediction continues to evolve, the proposed method will be an essential consideration for researchers and practitioners seeking to optimize building design and operation.
Recommendations
- ✓ Future research should investigate the application of the proposed method in various climate zones and regions to further validate its generalizability.
- ✓ The model's potential for integration with Building Information Modeling (BIM) and other building design tools should be explored to enhance its practical application.