Bayesian LSTM for indoor temperature modeling

• URL: http://arxiv.org/abs/2504.03350v1
• Date: Fri, 04 Apr 2025 11:07:23 GMT
• Title: Bayesian LSTM for indoor temperature modeling
• Authors: Emma Hannula, Arttu Häkkinen, Antti Solonen, Felibe Uribe, Jana de Wiljes, Lassi Roininen,
• Abstract summary: We propose a Bayesian Long Short-Term Memory architecture for indoor temperature modeling.<n>Our experiments across 100 real-world buildings demonstrate that the Bayesian LSTM outperforms an industrial physics-based model in predictive accuracy.<n>This work advances data-driven heating control by balancing predictive performance with the transparency and reliability required for real-world heating MPC applications.
• Score: 0.0
• License: http://creativecommons.org/licenses/by/4.0/
• 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.

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