Name
Title | ||
|---|---|---|
Knowledge Discovery from the Statistical Analysis of On-Site Photovoltaic System Data |
Abstract | ||
|---|---|---|
This paper presents some novel ideas and findings to assist data analysts and operators in discovering specific situations from the analysis of photovoltaic data collected in the field. The daily time series of solar irradiance and active power production are transformed into probability distribution functions (PDFs), then the skewness of the PDF is assessed as a potential indicator of orientation of the PV system in directions different from South. The relevant PDFs corresponding to bright days are identified by applying a clustering procedure. The combined calculation of skewness and correlation between solar irradiance and active power data is also used to discover specific cases in which high shadowing occurs in particular days of the year and at particular times. The analyses are carried out by using data collected on-site in PV systems installed at different locations. |
Year | DOI | Venue |
|---|---|---|
2022 | 10.1109/RTSI55261.2022.9905207 | 2022 IEEE 7th Forum on Research and Technologies for Society and Industry Innovation (RTSI) |
Keywords | DocType | ISSN |
Photovoltaic system,experimental data,time series,clustering,statistics,skewness | Conference | 2687-6809 |
ISBN | Citations | PageRank |
978-1-6654-9740-4 | 0 | 0.34 |
References | Authors | |
4 | 5 | |
Name | Order | Citations | PageRank |
|---|---|---|---|
| Gianfranco Chicco | 1 | 0 | 0.34 |
| Alessandro Ciocia | 2 | 0 | 0.34 |
| Andrea Mazza | 3 | 0 | 0.34 |
| Radu Porumb | 4 | 0 | 0.34 |
| Filippo Spertino | 5 | 0 | 0.34 |