The potential of Model Predictive Control in buildings has been shown many times, being successfully used to achieve various goals, such as minimizing energy consumption or maximizing thermal comfort. However, mass deployment has thus far failed, in part because of the high engineering cost of obtaining and maintaining a sufficiently accurate model. This can be addressed by using adaptive data-driven approaches. The idea of using behavioral systems theory for this purpose has recently found traction in the academic community. In this study, we compare variations thereof with different amounts of data used, different regularization weights, and different methods of data selection. Autoregressive models with exogenous inputs (ARX) are used as a well-established reference. All methods are evaluated by performing iterative system identification on two long-term data sets from real occupied buildings, neither of which include artificial excitation for the purpose of system identification. We find that: (1) Sufficient prediction accuracy is achieved with all methods. (2) The ARX models perform slightly better, while having the additional advantages of fewer tuning parameters and faster computation. (3) Adaptive and non-adaptive schemes perform similarly. (4) The regularization weights of the behavioral systems theory methods show the expected trade-off characteristic with an optimal middle value. (5) Using the most recent data yields better performance than selecting data with similar weather as the day to be predicted. (6) More data improves the model performance.

翻译：模型预测控制在建筑领域的潜力已被多次证明，其成功用于实现多种目标，例如最小化能耗或最大化热舒适度。然而，大规模部署至今未能实现，部分原因是获取和维护足够精确模型的高工程成本。通过采用自适应数据驱动方法可解决这一问题。近期，学术界开始关注利用行为系统理论实现此目标的思路。本研究比较了该理论的不同变体，包括不同数据使用量、不同正则化权重及不同数据选择方法。以外生输入的自回归模型（ARX）作为成熟的参考基准。所有方法均通过对两套实际居住建筑的长期数据集进行迭代系统识别评估，这些数据集均未包含为系统识别而施加的人工激励。研究发现：（1）所有方法均能达到足够的预测精度；（2）ARX模型表现略优，且具有调参参数更少、计算速度更快的额外优势；（3）自适应与非自适应方案表现相似；（4）行为系统理论方法的正则化权重呈现预期的最优中间值权衡特征；（5）使用最新数据比选择与预测日天气相似的数据效果更佳；（6）更多数据能提升模型性能。