Model Predictive Control (MPC) is a model-based technique widely and successfully used over the past years to improve control systems performance. A key factor prohibiting the widespread adoption of MPC for complex systems such as buildings is related to the difficulties (cost, time and effort) associated with the identification of a predictive model of a building. To overcome this problem, we introduce a novel idea for predictive control based on historical building data leveraging machine learning algorithms like regression trees and random forests. We call this approach Data-driven model Predictive Control (DPC), and we apply it to three different case studies to demonstrate its performance, scalability and robustness. In the first case study we consider a benchmark MPC controller using a bilinear building model, then we apply DPC to a data-set simulated from such bilinear model and derive a controller based only on the data. Our results demonstrate that DPC can provide comparable performance with respect to MPC applied to a perfectly known mathematical model. In the second case study we apply DPC to a 6 story 22 zone building model in EnergyPlus, for which model-based control is not economical and practical due to extreme complexity, and address a Demand Response problem. Our results demonstrate scalability and efficiency of DPC showing that DPC provides the desired power curtailment with an average error of 3%. In the third case study we implement and test DPC on real data from an off-grid house located in L’Aquila, Italy. We compare the total amount of energy saved with respect to the classical bang-bang controller, showing that we can perform an energy saving up to 49.2%. Our results demonstrate robustness of our method to uncertainties both in real data acquisition and weather forecast.

模型预测控制（MPC）是一种基于模型的技术，在过去的几年中广泛使用并成功地用于提高控制系统的性能。禁止在诸如建筑物之类的复杂系统中广泛采用MPC的关键因素与确定建筑物的预测模型相关的困难（成本，时间和精力）有关。为了克服这个问题，我们引入了一种基于历史建筑数据的预测控制新思路，该历史数据利用了诸如回归树和随机森林之类的机器学习算法。我们称这种方法为数据驱动模型预测控制（DPC），并将其应用于三个不同的案例研究，以证明其性能，可伸缩性和鲁棒性。在第一个案例研究中，我们考虑使用双线性构建模型的基准MPC控制器，然后将DPC应用于从该双线性模型模拟的数据集，并仅基于数据得出控制器。我们的结果表明，与应用于完全已知的数学模型的MPC相比，DPC可以提供可比的性能。在第二个案例研究中，我们将DPC应用于EnergyPlus中的一个6层22层建筑模型，该模型由于极端复杂而基于模型的控制既不经济又不实用，并解决了需求响应问题。我们的结果证明了DPC的可扩展性和效率，表明DPC提供了所需的功率缩减，平均误差为3％。在第三个案例研究中，我们根据位于意大利拉奎拉（L'Aquila）的离网房屋的真实数据实施并测试DPC。我们比较了传统bang-bang控制器所节省的能源总量，表明我们最多可以节省49.2％的能源。我们的结果证明了我们的方法对于真实数据采集和天气预报中的不确定性的鲁棒性。