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

Bing Dong, Reisa Widjaja, Wenbo Wu, Zhi Zhou

Research output: Contribution to journalReview articlepeer-review

12 Scopus citations


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.

Original languageEnglish (US)
Pages (from-to)885-907
Number of pages23
JournalBuilding Simulation
Issue number4
StatePublished - Aug 2021


  • building design and controls
  • model comparison
  • weather forecasting

ASJC Scopus subject areas

  • Building and Construction
  • Energy (miscellaneous)


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