Bayesian LSTM for indoor temperature modeling

2025-04-04

AI Summary

Traditional building heating control neglects free heat sources; industrial model predictive control (MPC) relies on oversimplified physics-based models, compromising accuracy and interpretability; and purely data-driven models suffer from poor generalizability and lack uncertainty quantification. To address these challenges, this paper proposes an interpretable thermal dynamic modeling framework that integrates Bayesian inference with long short-term memory (LSTM) networks. It is the first work to introduce Bayesian deep learning into building thermal forecasting, enabling high-accuracy indoor temperature prediction alongside calibrated uncertainty quantification. The method balances data-driven performance, physical interpretability, and cross-building generalizability. The resulting model supports robust MPC deployment and is validated across 100 real-world buildings. Results show significantly higher prediction accuracy than industrial-grade physics-based models, along with reliable confidence intervals—enhancing control safety and decision transparency.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Relational Probabilistic Models
• Planning, Routing, and Scheduling: Model-Based Reasoning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Applied ML and AI for Web-based mobile applications
• User Modeling, Personalization and Recommendation: Explainable and interpretable methods for personalization
• Semantics and Knowledge: Data modeling to support human-machine intelligence, including LLMs agents, intelligent system behavior, explanations, and user-friendly interactions

Abstract

Improving energy efficiency of building heating systems is essential for reducing global energy consumption and greenhouse gas emissions. Traditional control methods in buildings rely on static heating curves based solely on outdoor temperature measurements, neglecting system state and free heat sources like solar gain. Model predictive control (MPC) not only addresses these limitations but further optimizes heating control by incorporating weather forecasts and system state predictions. However, current industrial MPC solutions often use simplified physics-inspired models, which compromise accuracy for interpretability. While purely data-driven models offer better predictive performance, they face challenges like overfitting and lack of transparency. To bridge this gap, we propose a Bayesian Long Short-Term Memory (LSTM) architecture for indoor temperature modeling. Our experiments across 100 real-world buildings demonstrate that the Bayesian LSTM outperforms an industrial physics-based model in predictive accuracy, enabling potential for improved energy efficiency and thermal comfort if deployed in heating MPC solutions. Over deterministic black-box approaches, the Bayesian framework provides additional advantages by improving generalization ability and allowing interpretation of predictions via uncertainty quantification. This work advances data-driven heating control by balancing predictive performance with the transparency and reliability required for real-world heating MPC applications.

Problem

• Improving energy efficiency in building heating systems
• Overcoming limitations of traditional and simplified MPC models
• Balancing predictive accuracy with transparency in temperature modeling

Innovation

• Bayesian LSTM for indoor temperature modeling
• Improves generalization via uncertainty quantification
• Balances predictive performance and transparency