KIT | KIT-Bibliothek | Impressum | Datenschutz

Prefeasibility study of photovoltaic power potential based on a skew-normal distribution

Kim, S. Y.; Sapotta, B.; Jang, G.; Kang, Y.-H.; Kim, H.-G.

Abstract:
Solar energy does not always follow the normal distribution due to the characteristics of natural energy. The system advisor model (SAM), a well-known energy performance analysis program, analyzes exceedance probabilities by dividing solar irradiance into two cases, i.e., when normal distribution is followed, and when normal distribution is not followed. However, it does not provide a mathematical model for data distribution when not following the normal distribution. The present study applied the skew-normal distribution when solar irradiance does not follow the normal distribution, and calculated photovoltaic power potential to compare the result with those using the two existing methods. It determined which distribution was more appropriate between normal and skew-normal distributions using the Jarque–Bera test, and then the corrected Akaike information criterion (AICc). As a result, three places in Korea showed that the skew-normal distribution was more appropriate than the normal distribution during the summer and winter seasons. The AICc relative likelihood between two models was more than 0.3, which showed that the difference between the two models was not extremely high. ... mehr

Open Access Logo


Verlagsausgabe §
DOI: 10.5445/IR/1000105866
Veröffentlicht am 24.02.2020
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Funktionelle Grenzflächen (IFG)
Publikationstyp Zeitschriftenaufsatz
Publikationsjahr 2020
Sprache Englisch
Identifikator ISSN: 1996-1073
KITopen-ID: 1000105866
Erschienen in Energies
Band 13
Heft 3
Seiten Article: 676
Schlagwörter global horizontal irradiance (GHI); photovoltaic power potential; normal distribution; skew-normal distribution; exceedance probabilities
Nachgewiesen in Web of Science
Scopus
KIT – Die Forschungsuniversität in der Helmholtz-Gemeinschaft
KITopen Landing Page