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Solar Irradiance Forecast Using Naïve Bayes Classifier Based on Publicly Available Weather Forecasting Variables

Kwon, Youngsung and Kwasinski, Alexis and Kwasinski, Andres (2019) Solar Irradiance Forecast Using Naïve Bayes Classifier Based on Publicly Available Weather Forecasting Variables. Energies, 12 (8). p. 1529. ISSN 1996-1073

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This paper develops an approach for two-day-ahead global horizontal irradiance (GHI) forecast using the naïve Bayes classifier (NB). Based on publicly available weather forecasting information about temperature, relative humidity, dew point, and sky coverage, they are used as a training set in NB classification with hourly resolution. To reduce having two times with the same GHI affecting the classification in the proposed model, two characteristics of the GHI under different weather conditions are considered: The daylight variation and diurnal cycle. More importantly, NB’s independence assumption-based on simple Bayes’ theorem makes the process speed faster and less constrained than other classification algorithms. The forecast performance is verified with several error criteria from established analytical practices using relevant statistics. Moreover, commonly used forecasting error criteria are discussed. This NB model shows improved results regarding error criteria and a good agreement for a clear day that satisfies the guideline for the evaluation of two-days-ahead forecast, when compared with other recent techniques.


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Item Type: Article
Status: Published
CreatorsEmailPitt UsernameORCID
Kwon, Youngsung
Kwasinski, Andres
Date: 23 April 2019
Date Type: Publication
Journal or Publication Title: Energies
Volume: 12
Number: 8
Publisher: MDPI AG
Page Range: p. 1529
DOI or Unique Handle: 10.3390/en12081529
Schools and Programs: Swanson School of Engineering > Electrical and Computer Engineering
Refereed: Yes
Uncontrolled Keywords: global horizontal irradiance, naïve Bayes classification, diurnal variation, kernel density estimation
ISSN: 1996-1073
Official URL:
Article Type: Research Article
Date Deposited: 20 Oct 2021 13:14
Last Modified: 20 Oct 2021 13:14


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