Abstract
The cooling load calculation of building envelopes is important for building design to realize building energy efficiency. A simplified way to predict the building envelope cooling loads is desperately needed to predict the cooling load of building envelopes with various construction layouts and to predict the energy-saving effect of various reconstruction measures for buildings. In light of this need a simplified calculation model for building envelope cooling loads is proposed in this paper. The model is based on dynamic hourly calculations using EnergyPlus. It considers almost all the thermal factors about the building envelope which may affect the building cooling load and it is studied by dimensional analysis. The equivalent window to wall ratio (EWWR) and building orientation factor are defined to study the building envelope cooling load. The cooling loads of twenty hypothetical buildings with various envelopes are predicated by EnergyPlus. With the results from EnergyPlus the simplified calculation model is developed by MATLAB. Then the newly developed model is validated by two typical actual buildings located in Central-South China. The results show the model is accurate enough to predict the building envelope cooling loads.
Highlights
The cooling load calculation of building envelopes is important for building design to realize building energy efficiency
By counting its window area and window orientation, its equivalent window to wall ratio can be obtained by Equation (13) and the result is EWWR = 0.504
A simplified calculation model for building envelope cooling loads has been present in this paper
Summary
The cooling load calculation of building envelopes is important for building design to realize building energy efficiency. Numerous researchers devote themselves to the study of building load calculations. Sanza et al introduced a new method for calculating conduction response factors in building multilayer constructions [4]. Cheng used deep learning algorithms to predict the short-term building cooling loads [5]. Ji and Xu estimated hourly cooling loads in commercial buildings using a thermal network model and electricity submetering data [6]. Hong and Gon proposed a cooling load prediction model considering the time-lag phenomenon based on multiple regression analysis [7]. Using extensive parametric energy simulations by Design Builder, Babak investigated the impact of geometric factors including building orientation, plan shape, plan depth, and window-to-wall ratio for the Energies 2018, 11, 1708; doi:10.3390/en11071708 www.mdpi.com/journal/energies
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
