作者: Gabriele Lobaccaro , Salvatore Carlucci , Silvia Croce , Rossana Paparella , Luca Finocchiaro
DOI: 10.1016/J.SOLENER.2017.04.015
关键词: Solar energy 、 Urban planning 、 Terraced house 、 Boosting (machine learning) 、 Primary energy 、 Urban morphology 、 Apartment 、 Environmental science 、 Solar potential 、 Civil engineering
摘要: Abstract The harvesting of solar energy still encounters many barriers in Scandinavia. This paper proposes a set urban planning recommendations to enhance the accessibility and potential thereby increase production from integrated active systems installed Nordic environment. In this work, analyses using DIVA-for-Rhino were conducted on two typical Norwegian residential housing types, row houses high-rise apartment blocks, maximize their an isolated scenario evaluate contributions indirect mutual reflections created by surroundings. effect buildings’ orientation, finishing materials facades, ground soil have been estimated geometrically simplified districts. numerical outcomes observed transferred into that applied task developing masterplan Ovre Rotvoll district, located Trondheim, Norway. Simulations run (i) apply recommendations, (ii) optimize district morphology, (iii) localize most suitable surfaces for installing systems. results demonstrated optimizing morphology (e.g., building height distance between buildings) choosing colors facades soil) early design phases, can be increased up 25% yield provide 55% total primary demand entire even climate.