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A hybrid deep transfer learning strategy for thermal comfort prediction in buildings

机译：建筑物热舒适预测的混合深度转移学习策略

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Since the thermal condition of living spaces affects the occupants' productivity and their quality of life, it is important to design effective heating, ventilation and air conditioning (HVAC) control strategies for better energy efficiency and thermal comfort. An essential step in HVAC control and energy optimization is thermal comfort modeling. Recently, data-driven thermal comfort models have been preferred over the Fanger's Predicted Mean Vote (PMV) model due to higher accuracy and ease of use. However, the unavailability of comprehensive labelled thermal comfort data from the occupants poses a significant modeling challenge. This paper addresses data inadequacy issues by adopting 'transfer learning' to leverage well learned knowledge from source domain (same climate zones) to target domain (different climate zone) where modeling data is sparse. Specifically, a Transfer Learning based Convolutional Neural Networks-Long Short Term Memory neural networks (TL CNN-LSTM) is designed for effective thermal comfort modeling that exploits the spatio-temporal relations in the thermal comfort data. The significant modeling parameters for TL CNN-LSTM are identified using the Chi-squared test. Further, the lack of sufficient samples across all thermal conditions in the available thermal comfort datasets was handled by Synthetic Minority Oversampling Technique (SMOTE). Experiments with two source (ASHRAE RP-884 and Scales Project) and one target (Medium US office) datasets demonstrate the ability of TL CNN-LSTM in achieving an accuracy of 55% with limited data in target buildings. The limitation of TL CNN-LSTM is its continued dependence on intrusive parameters and the challenges in assessing its adaptability to different climate zones.
机译：由于生活空间的热条件影响乘员的生产力和它们的生活质量，因此设计有效的加热，通风和空调（HVAC）控制策略，以实现更好的能效和热舒适性。 HVAC控制和能量优化的基本步骤是热舒适性建模。最近，由于更高的准确性和易用性，数据驱动的热舒适模型是优先于Fanger的预测平均投票（PMV）模型。然而，来自乘员的全面标有热舒适数据的不可用处造成显着的建模挑战。本文通过采用“转移学习”来利用从源域（相同的气候区）到目标域（不同气候区）的知识来解决数据不足的问题，其中建模数据稀疏。具体地，基于转移学习的卷积神经网络长短短期存储器神经网络（TL CNN-LSTM）被设计用于有效的热舒适性建模，用于利用热舒适数据中的时空关系。使用CHI平方测试识别TL CNN-LSTM的显着建模参数。此外，通过合成少数群体过采样技术（SMOTE）处理可用热舒适数据集中所有热条件的足够量的样品。使用两个来源（ASHRAE RP-884和SCALES项目）和一个目标（中式US Office）数据集的实验证明了TL CNN-LSTM在实现＆GT的准确性方面的能力，在目标建筑物中有限的数据。 TL CNN-LSTM的限制是其继续依赖侵入性参数和评估其对不同气候区适应性的挑战。

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