Sepideh Niknia

Evaluating a building’s performance and comfort after it has been occupied is crucial for identifying issues such as high energy consumption, particularly in older structures built before 1970s energy crises, like those found on university campuses. This study is part of a broader Post-Occupancy Evaluation (POE) project assessing four major academic buildings across Texas Tech University. It intends to enhance their performance, energy efficiency, and occupants’ comfort by using retrofit strategies and building energy modeling. Data on actual energy usage, construction details, operating schedules, and equipment were collected using spot and long-term measurement techniques to evaluate building performance and user comfort. Building energy models were developed using the Integrated Environment Solution- Virtual Environment (IESVE). Simulations explored potential retrofits, including passive design strategies and renewable energy options such as set-back for HVAC systems, double-glazing windows, LED lighting, and cool roofs. The results showed that specific retrofits, such as double-glazing windows, yielded 15% and 18% energy reduction in two buildings, achieving Energy Use Intensity(EUI) values of 133 and 125, respectively, in two buildings. Furthermore, combining different retrofits could lead to significant annual reductions in energy consumption of 15.32%, 19.53%, 17.52%, and 9.09% for each of the four buildings in this study. These findings highlight the value of POE in identifying opportunities for energy savings and performance improvements in academic settings, providing a framework for broader application in other educational environments.

Windows in buildings impact energy usage for temperature control through solar heat gain and enable natural light to reduce reliance on artificial lighting. Balancing solar heat gain and daylight utilization is a challenge, which can be addressed by employing automated or manual blind systems to manage daylight and enhance user comfort and energy efficiency. Additionally, accurate weather forecasts are essential for predicting energy-efficient strategies through individual building energy simulations, as weather conditions synergistically interact with occupant behavior to influence energy consumption patterns. This research aims to assess the energy consumption associated with manual and automated internal window blinds in medium-sized office buildings situated within two distinct and significant climate zones in the United States, considering both present and future climate change scenarios based on the IPCC report (RCP 4.5 - 8.5). Employing a simulation-based methodology, the study unveiled varying effectiveness levels of diverse window blind configurations contingent on the specific climate zones (e.g., 4A Mixed-Humid, 2B Hot-Dry). In different climate zones, on-site energy consumption alterations for heating and cooling become evident as temperatures escalate in the forthcoming years. Using simulation as the method and comparing RCP 4.5 and 8.5 scenarios reveals that automated blinds—a more efficient choice than manual blinds—significantly reduces cooling energy consumption, particularly under RCP 8.5 in a 4A Mixed-Humid zone. Rising temperatures in Climate Zone 2B Hot-Dry are a factor in increased energy requirements for cooling and decreased energy requirements for heating due to climate change. This study provides enlightening insights into the potential benefits of diverse window-covering strategies concerning energy conservation within varied climatic contexts for the future.