Integrating smart energy management solutions into building automation systems

加拿大的商业建筑供暖、通风和空调（HVAC）系统占加拿大总能源使用量的7%，占加拿大温室气体总排放量的6%。越来越多的文献证据表明，商业建筑暖通空调系统使用的约30%的能源由于配置不理想和控制硬件和软件维护不善而浪费。该项目与加拿大最大的建筑管理系统（BMS）硬件和软件制造商Delta Controls合作，旨在通过将智能和电网交互能源管理功能集成到为商业建筑服务的BMS中来消除大部分浪费。具体而言，该项目将改进申请人小组先前开发的故障检测和诊断以及虚拟计量算法，开发新的基于模型的预测控制和需求响应算法，并将它们统一为一个完整的工作流程。这些算法将通过使用建筑性能模拟（BPS）工具EnergyPlus及其Python能源管理系统环境作为沙盒来开发。随后，它们将在卡尔顿大学的一个活体实验室进行测试，以进行测量和验证。该项目的成果包括一套算法，通过智能和电网交互能源管理功能增强传统BMS软件的功能。算法的发展和示范将通过期刊和会议论文传播。两名研究生将接受HVAC系统控制和自动化、BPS和数据分析方面的高级技能培训。该算法有望将商业建筑的供暖和制冷能源消耗减少30%。目前使用合作伙伴的BMS软件的8200座建筑将立即获得这些好处，这些好处将扩展到加拿大50万商业和机构建筑中的许多建筑。

Commercial building heating, ventilation, and air conditioning (HVAC) systems in Canada account for 7% of Canada's total energy use and are responsible for 6% of Canada's total GHG emissions. Growing evidence in the literature indicates that about 30% of the energy used by commercial building HVAC systems is wasted due to suboptimal configuration and poor maintenance of controls hardware and software. In partnership with the largest Canadian building management system (BMS) hardware and software manufacturer, Delta Controls, this project aims to eliminate much of this waste through smart and grid-interactive energy management capabilities integrated into BMSs serving commercial buildings. Specifically, the project will improve fault detection and diagnostics and virtual metering algorithms previously developed by the applicant's group, develop new model-based predictive controls and demand response algorithms, and unify them as a complete workflow. The algorithms will be developed by using the building performance simulation (BPS) tool EnergyPlus and its Python energy management system environment as a sandbox. Later, they will be tested in a living-lab facility at Carleton University for measurement and verification.Outcomes of the project include a suite of algorithms augmenting traditional BMS software capabilities with smart and grid-interactive energy management capabilities. Development and demonstration of the algorithms will be disseminated via journal and conference papers. Two graduate students will be trained with advanced skills in controls and automation of HVAC systems, BPS, and data analytics. The algorithms are expected to reduce heating and cooling energy use in commercial buildings by 30%. While these benefits will be immediately available to 8,200 buildings currently using the partner's BMS software, the benefits will scale to many of Canada's half a million commercial and institutional buildings.