Abstract
The presented thesis is part of a larger research interest of the Building Physics Research Group of the Free University of Bozen-Bolzano. It is motivated by a strong demand to gain insight into the behavior of occupants in the built environment to reduce our environmental footprint. We live in a world with limited resources and an ever-increasing demand for living and working space. The sheer need for reduced consumption and a circular economy, along with political incentives and legal regulations, compels us to move towards buildings that are energy-efficient, designed from cradle to grave, affordable, resilient to their occupants, and able to adapt to changing climatic conditions. Buildings are created for the simple purpose of keeping their occupants in the comfort zone of adequate living conditions, providing them with all the amenities they need to work, recreate, provide for themselves, and, at a minimum, survive in otherwise uninhabitable climates. Conversely, it is the people who reside in a building who make the most crucial decisions. At the macro level, building practitioners suggest optimal materials and designs to render a building safe to live in, comfortable in all weather conditions, and equipped with building services to support living. At the micro level, occupants are the primary drivers of their homes and workplaces. Thermostat settings, the use of windows, shades, and lights, or simply the lifestyle they seek to maintain, have a substantial impact on the performance of buildings, often resulting in a significant gap between predicted and actual building performance. This thesis addresses both scales and aims to provide valuable macro-level information for practitioners to make detailed decisions on building designs that are resilient to the various demands imposed by behavioral patterns and driving styles. At the micro level, individual behavioral models have been compared for various aspects, such as their impact on optimal retrofit strategies. To feed these models with data, an approach to collect information on the use of building services is presented, given that a reliable occupant representation comes with the need for sufficient training data. To address these outlined problems, two strategies were adopted. First, the impact of behavioral models on building performance, occupant-perceived comfort, and the resilience of building retrofit strategies has been investigated. This is done through the analysis of occupant behavior in energy refurbishment of existing buildings. The main tools are dynamic building energy simulation combined with multi-objective optimization. The results have been analyzed through Pareto difference metrics to quantify the influence of behavioral models on energy consumption, cost, and comfort. Two reference scenarios – in a heating and cooling-dominated climate – have been tested to observe the results under different boundary conditions. This led to an in-depth understanding of the extent to which behavioral models influence the estimated performance of an optimal retrofit solution and quantified robustness and resilience towards different behavioral patterns. In addition, the sensitivity of retrofit strategies to the initially assumed behavior has been explored, highlighting the importance of user behavior. The second part of the thesis focuses on monitoring strategies to deepen the understanding of occupant behavior. To fulfill the need for ground-proof training data, this work describes a new measurement approach implemented to provide continuous and non-intrusive monitoring of the window's opening angle, shading positions, and light operation to determine the net air exchange area for ventilation. A device and post-processing algorithm are developed for this purpose, and a monitoring campaign over 6 months has been conducted to validate and showcase the monitoring system. The methodology focuses on the design of hardware components and, in particular, a post processing algorithm that takes into account the state of the art of image post-processing using object identification and establishing a dataset able to train machine learning algorithms. The findings demonstrate that the performance indicators vary strongly with each behavioral model, severely compromising the competing objectives of energy demand and cost and resulting in major differences in indoor comfort conditions. The importance of realistic user behavior modeling is highlighted to prevent misleading conclusions about optimal solutions in the assessment of energy efficiency measures. Probabilistic behavior models are highly sensitive to variations in operating conditions, even leading to a positive rebound effect for certain retrofit strategies. These optimal solutions defined through probabilistic models are not expected to be very resilient to the ventilation rate, showing a potential for performance gaps. The importance of realistic user behavior representation is highlighted to raise awareness about its influence on the full potential of retrofitting a building, perhaps excluding those solutions that could majorly improve comfort. The results of the second part show that the proposed monitoring system can detect window opening angles and shutter positions, allowing us to quantify the advantage of continuous monitoring. The explored application is the direct use of the obtained data to calculate the natural ventilation defined by the net exchange area, as presented in the European Standard EN 16798-7. Especially, the simultaneous non-intrusive monitoring of multiple windows, shadings, and lights at once, alongside the ease of installation, makes the presented device a promising alternative to conventional sensors. The practical research implications are to be aware of the major impact behavioral models have on the optimal solution of a building retrofit. Practitioners should avoid solely relying on standardized schedules for occupant behavior and evaluate suitable models and their predictors in their specific reference environment. Therefore, future applications of the proposed monitoring device include the development and calibration of accurate behavioral models based on the obtained data to analyze and suggest improvements for building retrofit strategies. The presented concepts could, at little or no cost, result in reduced energy demand in buildings while accounting for the need for comfortable environments. In the current stage of the climate crisis, even modest improvements can be of considerable interest to designers and developers.