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Review Article

Review of onsite temperature and solar forecasting models to enable better building design and operations

Bing Dong1, Reisa Widjaja2, Wenbo Wu2, Zhi Zhou3( )
Department of Mechanical & Aerospace Engineering, Syracuse University, Syracuse, NY 13244, USA
Department of Management Science & Statistics, University of Texas at San Antonio, San Antonio, TX 78249, USA
Energy Systems Division, Argonne National Laboratory, Lemont, IL 60439, USA
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Abstract

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.

Keywords

weather forecastingbuilding design and controlsmodel comparison

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Building Simulation
Volume 14 Issue 4,
August 2021
Pages 885-907
DOI: 10.1007/s12273-020-0759-2
Cite this article:
Dong B, Widjaja R, Wu W, et al. Review of onsite temperature and solar forecasting models to enable better building design and operations. Building Simulation, 2021, 14(4): 885-907. https://doi.org/10.1007/s12273-020-0759-2

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Received: 19 May 2020
Accepted: 13 December 2020
Published: 24 February 2021
© Tsinghua University Press and Springer-Verlag GmbH Germany, part of Springer Nature 2021
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