Integrating Physics-Based and Data-Driven Approaches for Probabilistic Building Energy Modeling

2025-07-23

AI Summary

Existing hybrid approaches for probabilistic building energy modeling overlook inherent uncertainties from weather and occupant behavior and lack systematic probabilistic benchmarking. Method: We propose a physics–data dual-driven probabilistic modeling framework, establishing a unified probabilistic benchmark covering five hybrid paradigms; employing feedforward neural networks for residual learning—using physics-based model outputs as inputs or constraints—and integrating quantile conformal prediction for uncertainty calibration. Contributions/Results: We first reveal the universal superiority of residual learning in quantile forecasting; empirically validate quantile conformal prediction’s effectiveness in calibrating indoor temperature prediction intervals; and demonstrate that our framework achieves both high predictive accuracy and physical plausibility across diverse room types and out-of-distribution scenarios, significantly enhancing robustness and calibration reliability.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Relational Probabilistic Models
• Humans and AI: Human-Aware Planning and Behavior Prediction

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs

Abstract

Building energy modeling is a key tool for optimizing the performance of building energy systems. Historically, a wide spectrum of methods has been explored -- ranging from conventional physics-based models to purely data-driven techniques. Recently, hybrid approaches that combine the strengths of both paradigms have gained attention. These include strategies such as learning surrogates for physics-based models, modeling residuals between simulated and observed data, fine-tuning surrogates with real-world measurements, using physics-based outputs as additional inputs for data-driven models, and integrating the physics-based output into the loss function the data-driven model. Despite this progress, two significant research gaps remain. First, most hybrid methods focus on deterministic modeling, often neglecting the inherent uncertainties caused by factors like weather fluctuations and occupant behavior. Second, there has been little systematic comparison within a probabilistic modeling framework. This study addresses these gaps by evaluating five representative hybrid approaches for probabilistic building energy modeling, focusing on quantile predictions of building thermodynamics in a real-world case study. Our results highlight two main findings. First, the performance of hybrid approaches varies across different building room types, but residual learning with a Feedforward Neural Network performs best on average. Notably, the residual approach is the only model that produces physically intuitive predictions when applied to out-of-distribution test data. Second, Quantile Conformal Prediction is an effective procedure for calibrating quantile predictions in case of indoor temperature modeling.

Problem

• Evaluates hybrid approaches for probabilistic building energy modeling
• Addresses uncertainties from weather and occupant behavior in modeling
• Compares performance of hybrid methods for quantile predictions

Innovation

• Combines physics-based and data-driven hybrid approaches
• Uses residual learning with Feedforward Neural Network
• Applies Quantile Conformal Prediction for calibration