Université Paris-Est, LISSI Laboratory EA 3956, Senart-FB Institute of Technology, Campus de Senart, 36-37 Rue Charpak-F-77567 Lieusaint, France.
Sensors (Basel). 2020 Feb 16;20(4):1071. doi: 10.3390/s20041071.
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.
由于涉及过程的固有非线性性质以及这些过程中涉及的动态参数的强非线性，对居住空间的加热特性进行建模和控制仍然是具有挑战性的任务。尽管如今，自适应加热控制器代表了智能建筑能源管理系统（SBEMS）的重要需求，并且在优化能源效率方面具有吸引力，但遗憾的是，能够处理实际居住空间加热过程复杂性的模型的泄漏意味着大多数 SBEMS 中实施的控制策略仍然是传统的。在这种情况下，并且考虑到居住空间的占用率（即用户或居民）可能会影响所涉及的居住空间的模型和发出的加热控制策略，我们研究了设计和实施基于数据驱动的机器学习的方法，以识别建筑物的居住空间动态加热行为，同时考虑到加热空间的占用情况（由居民占用）。实际上，所提出的建模策略一方面利用了非线性自回归外部（NARX）模型的时间序列预测能力，另一方面利用了多层感知机（MLP）的学习和泛化技能。该方法已被实施并应用于建模位于巴黎东部克雷泰伊大学（UPEC） Senart 校区的一栋五层实际建筑物的居住空间的动态加热行为，同时考虑了它们的占用情况（由这座公共建筑的用户使用）。报告并讨论了所研究的混合基于机器学习的方法的准确性和成瘾性的评估结果。
