Operation of environmentally responsive building components requires rapid prediction of the optimal adaptation of geometric shapes and positions, and such responsive configuration needs to be identified during the design process as early as possible. However, building simulation practices to characterize optimized shapes of various geometric design candidates are limited by complex simulation procedures, slow optimization, and lack of site information. This study suggests a practical approach to the design of responsive building façades by integrating on-site sensors, building performance simulation (BPS), machine-learning, and 3D geometry modeling on a unified design interface. To this end, a novel and efficient hybrid optimization algorithm, tabu-based adaptive pattern search simulated annealing (T-APSSA), was developed and integrated with wireless sensor data communication (using nRF24L01 and ESP8266 WiFi modules) on a parametric visual programming language (VPL) interface Rhino Grasshopper (0.9.0076, McNeel, Seattle, USA). The effectiveness of T-APSSA for early-stage BPS and optimal design is compared with other metaheuristic algorithms, and the proposed framework is validated by experimental optimal envelope (window shading) designs for single (daylight) and multiple (daylight and energy) objectives. Test results demonstrate the improved efficiency of T-APSSA in calculations (two to four times faster than other algorithms). This T-APSSA-integrated sensor-enabled design optimization practice supports rapid BPS and digital prototyping of responsive building façade design.
The authors would like to thank colleagues in CARTA, Florida International University for their encouragements and supports.This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Korea government (MEST) (NRF-2018R1C1B5084299)elucidating a single building component (façade window), focusing only on interior illuminance. characterize a variety of building performances, including energy use, human behavior, and so forth. Accordingly, future studies need to follow up with further experiments that incorporate various This understanding can be strengthened with various applications, yet this study is limited to types of sensor-captured data, co-simulation of lighting, energy use, indoor air-flow, algorithmic elucidating a single building component (façade window), focusing only on interior illuminance. advancements to set dynamic parameters (annealing temperature and cooling rate of SA, TL size, Accordingly, future studies need to follow up with further experiments that incorporate various types of etc.), and MOBPO with different forms of façade/shading design modules. sensor-captured data, co-simulation of lighting, energy use, indoor air-flow, algorithmic advancements to seAtudtyhnoarmCoicnptraibraumtioentesr: sH(wananngea Yliin dgetseigmnpede rtahtius rsetuadnyd acnodoldinevgerloapteedo fthSeA a,lTgoLristihzme,, emtca.n),aagnindgMovOerBaPllO work withpdroicffeesrse. nMtif-oJirnmKsimof afnadça Yduer/ishKaimdincognddeuscitgedn dmeosidgunleexsp. eriments. Sun-Sook Kim and Kyu-In Lee reviewed to verify research outcomes, collaborating to complete the manuscript with Hwang Yi. Author Contributions: H.Y. designed this study and developed the algorithm, managing overall work process. M.-J.FKu.nadnidngY:.TKh.iscownodrukctwedas dseuspigpnoretexdp ebryimtheen tNs.atiSo.n-Sa.lKR.easneadrcKh.-FI.oLu.nrdeavtiieowneodf Ktoorveear i(fNyRreFs) egarracnht ofuuntcdoemd ebsy, the Korea government (MEST) (NRF-2018R1C1B5084299). Funding: This work was supported by the National Research Foundation of Korea (NRF) grant funded by the Acknowledgments: The authors would like to thank colleagues in CARTA, Florida International University for Korea government (MEST) (NRF-2018R1C1B5084299). their encouragements and supports. Acknowledgments: The authors would like to thank colleagues in CARTA, Florida International University for Conflicts of Interest: The authors declare no conflict of interest.
