Intelligent building technology for building operation, called the optimization process, is developed in this thesis. The optimization process, when installed in parallel with a building's central control system, will permit the optimal operation of HVAC systems. Using this proposed optimization process, the supervisory control strategy set points, such as supply air temperature, supply duct static pressure, chilled water supply temperature, supply C02 concentration (or minimum outdoor ventilation), reheat (or zone supply air temperature), and zone air temperature are optimized with respect to energy use and thermal comfort.ududThe multi-objective genetic algorithm that is developed and validated using mathematic and simplified HVAC system problems is used to solve the optimization problem. Detailed VAV components are developed and validated against the monitored data of the investigated existing HVAC system. Adaptive VAV component models using artificial neural networks are also developed for working with most existing building energy management control systems.ududA ventilation control strategy using supply CO2 concentration set points is developed and integrated into the optimization process. The strategy allows the on-line control of the outdoor air in response to actual building occupancy while ensuring the ventilation requirements of each individual zone.ududThe optimization of the existing HVAC system is done for the three different weather weeks taken from July 2002, February 2003, and May 2003. The simulation results show that by comparing actual and optimal energy use, the on-line implementation of the optimization process could save energy by 19.5%, 50%, and 40%, respectively, while satisfying minimum zone airflow rates and zone thermal comfort. It also shows that the application of a two-objective optimization problem could help control daily energy use or daily building thermal comfort while providing further energy use savings as compared to the one-objective optimization problem.
展开▼
机译：本文开发了一种智能的楼宇运行技术，称为优化过程。与建筑物的中央控制系统并行安装时，优化过程将使HVAC系统达到最佳运行状态。使用此建议的优化过程，监督控制策略设定点，例如送风温度，送风管道静压，冷冻水供给温度，送风CO2浓度（或最小室外通风），再热（或区域送风温度）和区域在能源使用和热舒适性方面优化空气温度。 ud ud使用数学和简化的HVAC系统问题开发和验证的多目标遗传算法用于解决优化问题。根据研究的现有HVAC系统的监视数据，开发并验证了详细的VAV组件。还开发了使用人工神经网络的自适应VAV组件模型，以与大多数现有的建筑能源管理控制系统配合使用。 ud ud开发了使用供应CO2浓度设定点的通风控制策略，并将其集成到优化过程中。该策略允许对室外空气进行在线控制，以响应实际的建筑物占用情况，同时确保每个区域的通风要求。 ud ud针对从2002年7月开始的三个不同天气周对现有HVAC系统进行了优化。 ，2003年2月和2003年5月。仿真结果表明，通过比较实际和最佳能源使用情况，优化过程的在线实施可以在满足最小区域的同时分别节省19.5％，50％和40％的能源。气流速率和区域热舒适度。它还表明，与一目标优化问题相比，应用两目标优化问题可以帮助控制日常能源使用或建筑物的日常热舒适性，同时进一步节省能源使用。
展开▼
