SmartEMS: Applying machine learning in building energy management systems

SmartEMS项目将开发适用于非住宅建筑能源管理系统的数据分析和机器学习方法，并在真实建筑中进行测试。最近，机器学习能力的显著提高使训练和部署这样的算法以解决实际挑战成为可能。建筑中的复杂能源系统提出了许多这样的挑战，从设定点优化到预测控制。SES Inc.有能力和客户基础来利用这一点。有两种核心方法：离线从数据中学习和实时预测控制。前者将通过分析业务数据确定潜在趋势和关注领域；后者将开发和培训机器学习控制器，以根据天气预报改善业务。SmartEMS将使用SES Inc.在以前的项目中成功使用的开源系统和协议(VOLTRON、BACnet)。基于Python的开源最先进的机器学习库将与其他基于Python的数据分析和可视化库一起使用。远程可访问的接口硬件(基本PC；Raspberry PI)将部署在3个测试建筑(一个大学校园和两个SES Inc.的客户端)。CANARIE的云计算将用于该过程的计算密集型部分。其成果将是基于最新学术研究的可商业部署的解决方案；其中的部分也将以开源形式发布。这将为正在进行的合作奠定基础。这一概念在改善商业建筑的能源使用、排放和舒适性方面具有巨大的潜力。

The SmartEMS project will develop data analysis and machine learning approaches suitable for incorporationin non-residential building energy management systems, and test them in real buildings. Dramatic recentimprovements in the power of machine learning have made it possible to train and deploy such algorithms tosolve practical challenges. Complex energy systems in buildings present many such challenges, from set-pointoptimization to predictive control. SES Inc. has the capabilities and client-base to take advantage of this.There are two core approaches: offline learning from data, and real-time predictive control. The former willidentify underlying trends and areas of concern by analysis of operational data; the latter will develop and trainmachine learning controllers that will improve operation based on weather predictions. The two together candeliver a highly flexible, robust solution.SmartEMS will use open-source systems and protocols (VOLTTRON, BACnet) that have been successfullyused by SES Inc. on previous projects. Open-source state of the art machine learning libraries based in Python(scikit-learn, TensorFlow) will be used, along with other Python-based data analysis and visualisation libraries.Remotely accessible interface hardware (bare-bones PCs; Raspberry Pi) will be deployed in 3 test buildings(one university campus and two clients of SES Inc.). Cloud computing from CANARIE will be used forcomputationally intensive parts of the process.The output will be a commercially deployable solution based on the latest academic research; parts of this willalso be released as open-source. This will form the basis for an ongoing collaboration. The concept hassignificant potential to improve energy use, emissions and comfort in commercial buildings.