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Deep learning-based heat optimization techniques for forecasting indoor temperature changes

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Abstract

The traditional climate compensator technique requires extensive modifications and significantly relies on manual knowledge when it comes to winter heating in buildings. This paper suggests a deep-learning-based optimization procedure for heating systems to improve the initial control strategy. To begin, we recommend using a deep MTDN (Multiple Time Difference Network) to deduce the thermodynamic laws governing the dynamics of indoor temperature changes and make future predictions about the temperature in a given space. The network is and follows physical rules; next, M is used as a simulator, and the evaluation index representing the human thermal reaction issued as the appropriate reward item. Next, a strategy optimizer based on the SAC (Soft Actor-Critic) learning thought algorithm is used to train the simulator to realize a stable and excellent heating control strategy; finally, experiments are designed using real data from a Tianjin heat exchange station to evaluate the simulator’s ability to predict future outcomes. It is confirmed that the simulator not only has high prediction accuracy but also adheres to the laws of physics and that the strategy optimizer learned approach could guarantee more stable and comfortable indoor temperature in multiple periods of random sampling, as compared to the original strategy.

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  1. Computer Science and Engineering, Medi-Caps University, Indore, Madhya Pradesh, India

    Latika Jindal, Nitika Vats Doohan, Swati Vaidya, Hemlata Patel & Arpit Deo

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  1. Latika Jindal
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  2. Nitika Vats Doohan
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  3. Swati Vaidya
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Correspondence to Latika Jindal.

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Jindal, L., Doohan, N.V., Vaidya, S. et al. Deep learning-based heat optimization techniques for forecasting indoor temperature changes. Spat. Inf. Res. 32, 107–117 (2024). https://doi.org/10.1007/s41324-023-00546-w

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  • DOI: https://doi.org/10.1007/s41324-023-00546-w

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