Urban energy simulation is becoming more and more important in various areas like urban planning, architecture design and city management. It provides quantified insights for architects and governors to deliver energy-efficient approaches. There are already quite a few energy sim
...
Urban energy simulation is becoming more and more important in various areas like urban planning, architecture design and city management. It provides quantified insights for architects and governors to deliver energy-efficient approaches. There are already quite a few energy simulation software or engines on the market. Among these tools, Ladybug family, a series of open-source python packages have its advantages: easy to use, high level of customization and low cost of adoption. It could be run in Rhino Grasshopper, a visual programming interface widely used by architecture industry. Taking 3D geometry created in Rhino and local weather data, Ladybug tools (Ladybug and Honeybee) prepare simulation recipes to run energy simulation with validated software engines like EnergyPlus and OpenStudio. With all these advantages, when using Ladybug and Honeybee for urban energy simulation, there are two major flaws: it is tedious to build 3D models of all building blocks in Rhino one by one and many key parameters required by energy simulation have to be entered manually. This geometry creation and parameters entering process could be avoided when using CityGML data as input, as CityGML with EnergyADE data already has 3D geometry and energy-related attributes of city within its data model. In this research, a mapping table between required simulation parameters of Ladybug tool - Honeybee and CityGML with EnergyADE data model is created. Based on this mapping relationship, by following a database approach, all information stored in CityGML with EnergyADE data is retrieved and stored in tables of 3DCityDB and later queried in Rhino Grasshopper and used in data mapping and processing workflow. Energy simulation results could be saved back to database too. It is concluded that using CityGML with EnergyADE data as input for Honeybee tools is applicable as there is a sufficient mapping relationship between their data models. However, as Honeybee has certain restrictions on input geometry (shared surface areas should be independent surfaces and surfaces should be convex etc.) and it runs energy simulation of buildings not simultaneously but one by one, it is not efficient to use Honeybee for large scale urban energy simulation.