Localized Actual Meteorological Year File Creator (LAF): A tool for using locally observed weather data in building energy simulations

被引:12
作者
Bianchi, Carlo [1 ,2 ]
Smith, Amanda D. [1 ]
机构
[1] Univ Utah, Dept Mech Engn, Site Specif Energy Syst Lab, Salt Lake City, UT 84112 USA
[2] NREL, Golden, CO 80401 USA
基金
美国国家科学基金会;
关键词
Building energy modeling; EnergyPlus; Weather data; Microclimate; Meteorological year; URBAN MICROCLIMATE; DEMAND; CLIMATE; ACCOUNT; DESIGN; IMPACT;
D O I
10.1016/j.softx.2019.100299
中图分类号
TP31 [计算机软件];
学科分类号
081202 ; 0835 ;
摘要
The Localized Actual Meteorological Year File Creator (LAF) application provides web-based access to real meteorological data and processes it into a weather file suitable for building energy modeling. Building energy consumption is affected by what is inside the building (such as occupants, appliances, HVAC systems, etc.) and by what is outside: the weather conditions the building is exposed to. However, freely available weather data files are limited to a number of specific locations, usually airports, which are often located away from city centers where buildings are concentrated. The authors have developed a new tool that supports the investigation and quantification of micro-climate conditions on building energy consumption. LAF is built on the Python open-source programming language and has a Graphical User Interface (GUI) that allows users to create custom weather data files for building energy simulations. Many sets of actual meteorological year weather data for long time periods are publicly available online (such as the MesoWest database) for thousands of locations in the US. LAF selects weather data according to user specifications and automatically processes it through an API across multiple weather stations and multiple time periods. The user may easily select a specific location, time frame, and time step that best meets their needs. This article presents a useful tool for energy modelers, building designers and operators to assist with building performance analysis and optimization. (C) 2019 The Authors. Published by Elsevier B.V.
引用
收藏
页数:10
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