Advanced building controls and energy optimization for new constructions and retrofits rely on accurate weather data.Traditionally,most studies utilize airport weather information as the decision inputs.However,most b...Advanced building controls and energy optimization for new constructions and retrofits rely on accurate weather data.Traditionally,most studies utilize airport weather information as the decision inputs.However,most buildings are in environments that are quite different than those at the airport miles away.Tree cover,adjacent buildings,and micro-climate effects caused by the larger surrounding area can all yield deviations in air temperature,humidity,solar irradiance,and wind that are large enough to influence design and operation decisions.In order to overcome this challenge,there are many prior studies on developing weather forecasting algorithms from micro-to meso-scales.This paper reviews and complies knowledge on common weather data resources,data processing methodologies and forecasting techniques of weather information.Commonly used statistical,machine learning and physical-based models are discussed and presented as two major categories:deterministic forecasting and probabilistic forecasting.Finally,evaluation metrics for forecasting errors are listed and discussed.展开更多
基金This work was supported by the U.S.Department of Energy,Office of Energy Efficiency and Renewable Energy through its Building Technologies Office.The submitted manuscript has been created by UChicago Argonne,LLC,Operator of Argonne National Laboratory(“Argonne”)Argonne,a U.S.Department of Energy Office of Science laboratory,is operated under Contract No.DE AC02-06CH11357The views expressed in this article are the authors’own and do not necessarily represent the views of the U.S.Department of Energy or the United States Government.
文摘Advanced building controls and energy optimization for new constructions and retrofits rely on accurate weather data.Traditionally,most studies utilize airport weather information as the decision inputs.However,most buildings are in environments that are quite different than those at the airport miles away.Tree cover,adjacent buildings,and micro-climate effects caused by the larger surrounding area can all yield deviations in air temperature,humidity,solar irradiance,and wind that are large enough to influence design and operation decisions.In order to overcome this challenge,there are many prior studies on developing weather forecasting algorithms from micro-to meso-scales.This paper reviews and complies knowledge on common weather data resources,data processing methodologies and forecasting techniques of weather information.Commonly used statistical,machine learning and physical-based models are discussed and presented as two major categories:deterministic forecasting and probabilistic forecasting.Finally,evaluation metrics for forecasting errors are listed and discussed.
