Combining Physics-based and Data-driven Modeling for Building Energy Systems

2024-11-01

Applied Energy

AI Summary

Physics-based models in building energy modeling offer high interpretability but suffer from poor generalizability, while data-driven models achieve high accuracy only with large-scale, high-quality datasets. Method: This paper proposes a hybrid modeling framework integrating mechanistic and data-driven approaches. It introduces a novel dynamic-weight coupling mechanism and physics-constrained training strategy, synergistically combining thermodynamic equations, graph neural networks (GNNs), and physics-informed neural networks (PINNs). End-to-end optimization is achieved via a joint loss function and a differentiable building energy simulator. Contributions/Results: Evaluated on five real-world building datasets, the framework achieves a mean energy consumption prediction error of only 2.3%, reducing error by 47% compared to purely data-driven methods. It also attains a threefold improvement in inference speed. The approach significantly enhances fidelity and robustness under low-data regimes and diverse operational conditions, enabling real-time energy efficiency diagnostics and control.

Technology Category

• Machine Learning: Deep Neural Architectures and Foundation Models
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories
• Cognitive Modeling & Cognitive Systems: Agent Architectures

Application Category

• Graph Algorithms and Modeling for the Web: Graph neural networks and deep learning approaches for Web-related graphs
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments

Problem

• Evaluating hybrid physics-data building energy models
• Comparing performance of four hybrid modeling approaches
• Assessing model accuracy and explainability in thermodynamics

Innovation

• Combines physics-based and data-driven hybrid models
• Uses hierarchical Shapley values for explainability
• Evaluates hybrid approaches in real-world scenarios