@article {681021,
	title = {Using recurrent neural networks for localized weather prediction with combined use of public airport data and on-site measurements},
	journal = {Building and Environment},
	volume = {192},
	number = {107601},
	year = {2021},
	abstract = {Weather data is a crucial input for myriad applications in the built environment, including building energymodeling and daylight analysis. Building science practitioners and researchers have been able to select from avariety of weather files, such as Weather Year for Energy Calculation 2 (WYEC2) and the Typical MeteorologicalYear (TMY). However, commonly used weather files are typically synthesized to represent trends over a relativelylonger periods of time, and are often unable to accurately depict climatic conditions that result from localcontexts, such as the heat island effect, wind flow, even local temperature and relative humidity. This results indiscrepancies in building performance simulations.This study proposes a methodology using recurrent neural networks to generate synthetic localized weatherdata that are significantly more accurate and representative of local conditions than standard weather files. Thepredictions were validated against actual on-site measurements, and achieved a low mean square error of 2.96and over 185\% improvement in validation accuracy. Overall, the performance of selected models has shown over100\% improvements in test accuracy compared with standard weather files and weather station data at thenearest airport. The proposed methodology can be used to morph generic weather files to accurately representlocalized conditions, or generate localized data for a longer time span with only a subset of data available/collected. This is useful for downstream built environment applications, especially building energy modeling,since representative weather data capturing trends of temperature and other variables will result in enhancedaccuracies of the building energy models. The method can also be used in urban analysis pipelines to enhanceresilience against climate change.},
	author = {Han, J. M.* and Ang, Y.Q. and Malkawi, A. and Samuelson, H. W.}
}
