01804nas a2200265 4500008004100000022001300041245010900054210006900163260001600232300001100248490000800259520098000267653001601247653002201263653002101285653002501306653002401331100001401355700001901369700001301388700001701401700001601418700001901434856008501453 2019 eng d a0360132300aCross-source sensing data fusion for building occupancy prediction with adaptive lasso feature filtering0 aCrosssource sensing data fusion for building occupancy predictio cJan-09-2019 a1062800 v1623 a
Fusing various sensing data sources can significantly improve the accuracy and reliability of building occupancy detection. Fusing environmental sensors and wireless network signals are seldom studied for its computational and technical complexity. This study aims to propose an integrated adaptive lasso model that is able to extract critical data features for environmental and Wi-Fi probe dual sensing sources. Through rapid feature extraction and process simplification, the proposed method aims to improve the computational efficiency of occupancy detecting models. To validate the proposed model, an onsite experiment was conducted to examine two occupancy data resolutions, (real-time and four-level occupancy resolutions). The results suggested that, among all twelve features, eight features are most relevant. The mean absolute error of the real-time occupancy can be reduced to 2.18 and F1_accuracy is about 84.36% for the four-level occupancy.
10adata fusion10aFeature selection10aMachine learning10aoccupancy prediction10aPhysics-based model1 aWang, Wei1 aHong, Tianzhen1 aXu, Ning1 aXu, Xiaodong1 aChen, Jiayu1 aShan, Xiaofang uhttps://simulationresearch.lbl.gov/publications/cross-source-sensing-data-fusion01789nas a2200205 4500008004100000245013500041210006900176260001600245300000900261490000700270520100200277653012101279100001701400700001801417700001401435700001301449700001901462700001101481856009101492 2019 eng d00aRevealing Urban Morphology and Outdoor Comfort through Genetic Algorithm-Driven Urban Block Design in Dry and Hot Regions of China0 aRevealing Urban Morphology and Outdoor Comfort through Genetic A cJan-07-2019 a36830 v113 aIn areas with a dry and hot climate, factors such as strong solar radiation, high temperature, low humidity, dazzling light, and dust storms can tremendously reduce people’s thermal comfort. Therefore, researchers are paying more attention to outdoor thermal comfort in urban environments as part of urban design. This study proposed an automatic workflow to optimize urban spatial forms with the aim of improvement of outdoor thermal comfort conditions, characterized by the universal thermal climate index (UTCI). A city with a dry and hot climate—Kashgar, China—is further selected as an actual case study of an urban block and Rhino & Grasshopper is the platform used to conduct simulation and optimization process with the genetic algorithm. Results showed that in summer, the proposed method can reduce the averaged UTCI from 31.17 to 27.43 °C, a decrease of about 3.74 °C, and reduce mean radiation temperature (MRT) from 43.94 to 41.29 °C, a decrease of about 2.65 °C.
10adry and hot areas; outdoor thermal comfort; urban morphology; urban performance simulation; genetic algorithm-driven1 aXu, Xiaodong1 aYin, Chenhuan1 aWang, Wei1 aXu, Ning1 aHong, Tianzhen1 aLi, Qi uhttps://www.mdpi.com/2071-1050/11/13/3683https://www.mdpi.com/2071-1050/11/13/3683/pdf