We present a novel approach to obtaining dynamic nonlinear models using genetic programming (GP) for the model predictive control (MPC) of the indoor temperatures of buildings. Currently, the large-scale adoption of MPC in buildings is economically unviable due to the time and cost involved in the design and tuning of predictive models by expert control engineers. We show that GP is able to automate this process, and have performed open-loop system identification over the data produced by an industry grade building simulator. The simulated building was subject to an amplitude modulated pseudo-random binary sequence (APRBS), which allows the collected data to be sufficiently informative to capture the underlying system dynamics under relevant operating conditions.

In this initial report, we detail how we employed GP to construct the predictive model for MPC for heating a single-zone building in simulation, and report results of using this model for controlling the internal environmental conditions of the simulated single-zone building. We conclude that GP shows great promise for producing models that allow the MPC of building to achieve the desired temperature band in a single zone space.

我们提出了一种新的方法来获得动态非线性模型，该模型使用遗传编程（GP）对建筑物的室内温度进行模型预测控制（MPC）。当前，由于专家控制工程师在设计和调整预测模型中花费时间和成本，因此在建筑物中大规模采用MPC在经济上是不可行的。我们证明了GP能够使这一过程自动化，并且已经对工业级建筑模拟器产生的数据进行了开环系统识别。模拟建筑物要经过调幅的伪随机二进制序列（APRBS），该序列使收集的数据具有足够的信息性，可以捕获相关操作条件下的基础系统动态。

在此初始报告中，我们详细介绍了如何使用GP来为模拟中的单区域建筑供暖的MPC构建预测模型，并报告使用该模型控制模拟的单区域建筑的内部环境条件的结果。我们得出的结论是，GP显示出生产模型的巨大前景，该模型允许建筑物的MPC在单个区域空间中达到所需的温度带。