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首页> 美国卫生研究院文献>Sensors (Basel Switzerland) >Data-Driven Living Spaces’ Heating Dynamics Modeling in Smart Buildings using Machine Learning-Based Identification
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Data-Driven Living Spaces’ Heating Dynamics Modeling in Smart Buildings using Machine Learning-Based Identification

机译：基于机器学习的数据驱动的智能建筑中居住空间的供热动力学建模

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Modeling and control of the heating feature of living spaces remain challenging tasks because of the intrinsic nonlinear nature of the involved processes as well as the strong nonlinearity of the entailed dynamic parameters in those processes. Although nowadays, adaptive heating controllers represent a crucial need for smart building energy management systems (SBEMS) as well as an appealing perspective for their effectiveness in optimizing energy efficiency, unfortunately, the leakage of models competent in handling the complexity of real living spaces’ heating processes means the control strategies implemented in most SBEMSs are still conventional. Within this context and by considering that the living space’s occupation rate (i.e., by users or residents) may affect the model and the issued heating control strategy of the concerned living space, we have investigated the design and implementation of a data-driven machine learning-based identification of the building’s living space dynamic heating conduct, taking into account the occupancy (by the residents) of the heated space. In fact, the proposed modeling strategy takes advantage, on the one hand, of the forecasting capacity of the time-series of the nonlinear autoregressive exogenous (NARX) model, and on the other hand, from the multi-layer perceptron’s (MLP) learning and generalization skills. The proposed approach has been implemented and applied for modeling the dynamic heating conduct of a real five-floor building’s living spaces located at Senart Campus of University Paris-Est Créteil (UPEC), taking into account their occupancy (by users of this public building). The obtained results assessing the accuracy and addictiveness of the investigated hybrid machine learning-based approach are reported and discussed.
机译：由于所涉及过程的固有非线性性质以及这些过程中所涉及的动态参数的强烈非线性，对居住空间供暖特征进行建模和控制仍然是一项艰巨的任务。尽管如今，自适应供热控制器代表了对智能建筑能源管理系统（SBEMS）的迫切需求，以及其在优化能源效率方面的有效性的诱人视角，但不幸的是，泄漏了处理实际居住空间供暖复杂性的模型流程意味着大多数SBEMS中实施的控制策略仍然是常规的。在此背景下，考虑到居住空间的占用率（即用户或居民）可能会影响有关居住空间的模型和已发布的加热控制策略，我们研究了数据驱动的机器学习的设计和实现基于建筑物的居住空间动态供暖行为的识别，同时考虑供暖空间的占用（居民）。实际上，提出的建模策略一方面利用了非线性自回归外生（NARX）模型的时间序列的预测能力，另一方面利用了多层感知器（MLP）的学习和概括技巧。拟议的方法已经实施，并已用于对位于巴黎埃斯特·克雷泰伊大学（UPEC）Senart校区（UPEC）的真实五层建筑的居住空间的动态供暖进行建模，并考虑到其使用情况（由该公共建筑的用户） 。报告并讨论了评估所研究的基于混合机器学习的方法的准确性和成瘾性的结果。

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