Calculating energy consumption plays an important role in the built environment and affects interior design. In the U.S., residential and commercial buildings accounted for 40% of national energy use in 2017, and educational institutions consumed substantial portions of the commercial sector’s electricity and natural gas (EIA, 2020). Biomimicry, an emerging field, seeks to mimic nature’s complex models to solve human challenges, such as the leaf‐inspired solar cell (Shahda, Elmokadem, & Abd Elhafeez, 2014). The term “biomimetic,” introduced by Otto H. Schmitt in 1969, emphasized utilizing biological knowledge forsustainability in architecture and design, and its applications span various sectors (Yetkin, 2021). Within this context, previous research introduced a novel biomimetic window system (Son, 2020), designed to cut down building energy usage.
This research investigated a biomimetic window system aimed at enhancing light penetration and energy savings in educational structures, particularly where is limited natural daylight access. The study simulated energy consumption for a university building in Indiana. This multifunctional building accommodates students and faculty, including offices and lecture rooms. Interestingly, many offices and lecture rooms are designed without windows due to their interior positioning. Given the building’s heavy daytime usage during academic semesters and its unique spatial layout, it serves as an optimal choice for examining disparities in energy consumption. Measurement and verification guidelines, including IPMVP, FEMP, and ASHRAE Guideline 14, advocate for a predetermined base model to accurately measure energy savings in building energy management projects. As directly measuring energy savings can be challenging, the base model should closely mirror actual energy consumption, with acceptable margins of error. Two key statistical indices assess this accuracy: the mean bias error (MBE) and the coefficient of variation of the root mean squared error (CV(RMSE)). MBE calculates the percentage error between actual and simulated energy consumption, while CV(RMSE) evaluates how well the base model matches the actual data, with a lower value indicating superior calibration. For example, an MBE for a building with actual energy consumption of 100,000 kWh and a simulated consumption of 80,000 kWh would be 20%. Simulations compared energy consumption data with and without the proposed window system. By comparing these findings, the research quantified not only the energy savings but also the associated financial benefits, utilizing 2023’s average electricity costs in Indiana.
The author validated the base model by deriving the MBE and CV(RMSE) values, contrasting simulation outcomes with the actual energy usage of a building powered by electricity, natural gas, and geothermal energy. Accordingly, when using monthly data for simulation, the 3D model is considered appropriate if the MBE is within ±5% and the CV(RMSE) is within 15%. Some differences naturally appeared between the simulation and actual data, with the MBE within ±5.2% and the CV(RMSE) at 11.87% which are the acceptable range based on ASHRAE Guideline 14 (ASHRAE, 2002). Such differences indicate the base model’s reliability. The biomimetic window system was integrated into the base model, and recalculated the energy consumption and compared with the energy consumption of the base model. To determine the system’s effectiveness, two calculations were made. The first calculation was a comparison between the actual and the simulated energy usage, estimating a potential annual savings of 55,433 kWh when implementing the window system. The second method was applying Indiana’s average electricity cost. This approach showed an annual savings of $6,663.13,signifying energy‐saving percentages of 14.61%. While comparisons between simulations and reality may not be wholly accurate, the data underscores the proposed system’s potential benefits.
