Lighting control systems have been traditionally employed to reduce energy use for lighting by, for example, maximizing daylight harvesting. When highly efficient light sources are installed and for tasks where maintaining target illuminance is particularly important, designers may decide to prioritize the latter together with energy use. In this context, the use of data-driven algorithms is emerging. In this paper different data-driven approaches are proposed as lighting control systems, to maximize daylight harvesting and to optimize energy consumption. The approaches employ experimental data of occupancy and lighting switch on/off events of a private side-lit office in an academic building. The office is later modeled in DIVA4Rhino to provide yearly illuminances and electric lighting dimming profiles. These data are used to implement data-driven optimal controls. Three different approaches have been employed: Regression Trees; Random Forests; Least Squares. Different lighting control strategies have been hypothesized based on installed Lighting Power Densities (LPD). Results show that Regression Trees outperforms both Least Squares and Random Forests, in terms of model accuracy and control performance.

传统上一直采用照明控制系统来减少照明的能源使用，例如，最大限度地收集日光。当安装了高效光源并且对于保持目标照度特别重要的任务时，设计人员可能会决定将后者与能源使用放在一起优先考虑。在这种情况下，数据驱动算法的使用正在兴起。在本文中，提出了不同的数据驱动方法作为照明控制系统，以最大限度地收集日光并优化能源消耗。这些方法采用了学术建筑中私人侧光办公室的占用和照明开关事件的实验数据。该办公室后来在 DIVA4Rhino 中建模，以提供年度照度和电灯调光配置文件。这些数据用于实施数据驱动的优化控制。已经采用了三种不同的方法： 回归树；随机森林；最小二乘法。已经根据安装的照明功率密度 (LPD) 假设了不同的照明控制策略。结果表明，在模型精度和控制性能方面，回归树优于最小二乘法和随机森林。