During the past years, studies have shown that energy-efficient buildings can provide effective means to achieve a range of global goals and bring multiple benefits to society, environment, and economy. As the most considerable energy and electricity consumption sector, the building industry has gradually fulfilled the vision of zero-energy or even net-positive-energy design and construction. Meanwhile, the large thermal mass and a significant amount of flexible load also make building the ideal target for energy storage and demand response to maintain grid stability. With the advent of smart devices and advanced technologies, more advanced design approaches have been developed to reduce the maintenance and utility costs while maintaining occupant comfort. In this dissertation, proactive approaches for the high-performance buildings are discussed in three phases, control system designing, monitoring-based commissioning, and existing building retrofits with renewables.
Modern buildings are equipped with monitoring systems to gather thousands of time series that capture and store real-time and historical information about occupancy, temperature, energy, and many other measurable operational data. Intelligent data analytics empower building management to transform into a predictive and proactive approach, which increases energy efficiency, improve occupant comfort, and predict maintenance. The powerful insights pulled from the data patterns enables the control strategy transformation from heuristic or rule-based to model-based.
Heating, ventilation, and air conditioning (HVAC) systems account for over 50% of the total energy use in buildings. They have enormous energy-saving potentials such that to be highlighted for commissioning and retrofitting. Chapter 2 and 3 present the physical model development for both energy and thermal dynamics within the building envelop. Details on the simulation-based, as well as the data-driven based model identification methods, are provided in Chapter 3. Given the unavailability of measurement, parametric, semiparametric, and nonparametric models are adopted to predict the thermal loads - those grey-box models achieve a balance between model interpretability and accuracy.
In Chapter 4, starting from the current best practice control strategy, demand-responsive rule-based control, in compliance with many building standards and codes, the proposed cost-responsive design strategy incorporates energy models, dynamically estimates the total cost among several candidate setpoints, and them iteratively moves in the direction of the least cost. The experiment results in Chapter 5 demonstrate the effectiveness of the proposed proactive control strategy and reveal significant energy cost reduction without compromising occupant comfort. This cost-effective approach is feasible to implement on a large scale with minimum interfering with the existing building management system infrastructure. Some practical challenges faced during deployment and testing are discussed and analyzed along with the possible generalization to other cases.
Chapter 6 documents issues and recommendations from a commissioning project concerning heating and ventilation issues arising from the occupant survey. In-depth statistical analyses of operational data trends over time can inform the possible fault and provide insights for future proactive maintenance. The case study demonstrates the feasibility of proactive monitoring-based commissioning. Furthermore, parametric methods of analyzing benefit and value in terms of cost, comfort, and energy are investigated on an existing office building to evaluate energy-efficient measures for retrofitting in Chapter 7.
