Enhancing real-time nonintrusive occupancy estimation in buildings via knowledge fusion network

Real-time nonintrusive occupancy estimation can maximize the use of existing sensors to infer occupant information in buildings with the advantages of fewer privacy concerns and fewer extra device costs. Recently, many deep learning architectures have proven effective in estimating occupancy directly from raw sensor data. However, some handcrafted features manually extracted from statistical and temporal domains might convey additional information for occupancy estimation. In this study, a novel knowledge fusion network for nonintrusive occupancy estimation is proposed to integrate knowledge from two streams, i.e. automatic knowledge stream from a deep learning architecture and handcrafted knowledge stream from manual feature engineering. Moreover, four different fusion modules are investigated to optimize the design of the fusion network. To verify the effectiveness of the proposed network, experiments are conducted in a dataset from the ASHRAE Global Occupant Behavior Database, which is collected from an office space with records of indoor environment parameters, occupant-building interactions, and contextual information. The results demonstrate the superiority of the proposed fusion network, which outperforms five representative algorithms. Furthermore, the ablation study underscores the benefits of knowledge fusion and occupant-building interaction information, showing that the proposed fusion network can enhance the occupancy estimation accuracy by 3.47 % to 9.24 %.Environmental & Climate Desig

Approaching nearly zero energy of PV direct air conditioners by integrating building design, load flexibility and PCM

The energy matching of PV driven air conditioners is influenced by building load demand and PV generation. Merely increasing energy performance of building or PV capacity separately may improve the energy balance on a large time resolution, the real-time energy mismatching problem is still serious. In this study, a coordinated optimization method of PV capacity, building design, and load flexibility is proposed for improving the real-time energy matching of PVAC system. Then, a methodology integrating data mining method (XG Boost) and parametric simulation was developed to identify the determinant parameters of PV system and building design, exploring feature importance and correlations. The results of XG Boost indicate that the PV capacity, shape factor, and SHGC are the most critical factors. Finally, based on the optimized building design, the PCM layer was applied to improve the real time energy matching. To achieve a goal of 90 % ZEP, the PCM capacity can be decreased by 50.4 % and 62.8 % in Guangzhou and Shanghai in the optimized building. Moreover, the PV capacity can be reduced by 23 % in Guangzhou. The findings of this study provide practical guidance for designing PVAC system coupling with building design and energy storage devices.Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.Environmental & Climate Desig