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

Youngsung Kwon, Alexis Kwasinski and Andres Kwasinski
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Youngsung Kwon: Department of Mechanical and Control Engineering, Handong Global University, Pohang 37554, Korea
Alexis Kwasinski: Department of Electrical and Computer Engineering, University of Pittsburg, Pittsburgh, PA 15261, USA
Andres Kwasinski: Rochester Institute of Technology, Rochester, NY 14623, USA

Energies, 2019, vol. 12, issue 8, 1-13

Abstract: 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.

Keywords: global horizontal irradiance; naïve Bayes classification; diurnal variation; kernel density estimation (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (8)

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