High-Resolution PV Forecasting from Imperfect Data: A Graph-Based Solution

Rafael E. Carrillo, Martin Leblanc, Baptiste Schubnel, Renaud Langou, Cyril Topfel and Pierre-Jean Alet
Additional contact information
Rafael E. Carrillo: CSEM PV-Center, Rue Jaquet-Droz 1, 2000 Neuchâtel, Switzerland
Martin Leblanc: CSEM PV-Center, Rue Jaquet-Droz 1, 2000 Neuchâtel, Switzerland
Baptiste Schubnel: CSEM PV-Center, Rue Jaquet-Droz 1, 2000 Neuchâtel, Switzerland
Renaud Langou: CSEM PV-Center, Rue Jaquet-Droz 1, 2000 Neuchâtel, Switzerland
Cyril Topfel: BKW AG, Viktoriaplatz 2, 3013 Bern, Switzerland
Pierre-Jean Alet: CSEM PV-Center, Rue Jaquet-Droz 1, 2000 Neuchâtel, Switzerland

Energies, 2020, vol. 13, issue 21, 1-17

Abstract: Operating power systems with large amounts of renewables requires predicting future photovoltaic (PV) production with fine temporal and spatial resolution. State-of-the-art techniques combine numerical weather predictions with statistical post-processing, but their resolution is too coarse for applications such as local congestion management. In this paper we introduce computing methods for multi-site PV forecasting, which exploit the intuition that PV systems provide a dense network of simple weather stations. These methods rely entirely on production data and address the real-life challenges that come with them, such as noise and gaps. Our approach builds on graph signal processing for signal reconstruction and for forecasting with a linear, spatio-temporal autoregressive (ST-AR) model. It also introduces a data-driven clear-sky production estimation for normalization. The proposed framework was evaluated over one year on both 303 real PV systems under commercial monitoring across Switzerland, and 1000 simulated ones based on high-resolution weather data. The results demonstrate the performance and robustness of the approach: with gaps of four hours on average in the input data, the average daytime NRMSE over a six-hour forecasting horizon (in 15 min steps) and over all systems is 13.8% and 9% for the real and synthetic data sets, respectively.

Keywords: multi-site photovoltaic forecasting; spatio-temporal correlation; graph signal processing; signal reconstruction (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: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (5)

Downloads: (external link)
https://www.mdpi.com/1996-1073/13/21/5763/pdf (application/pdf)
https://www.mdpi.com/1996-1073/13/21/5763/ (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:gam:jeners:v:13:y:2020:i:21:p:5763-:d:439556

Access Statistics for this article

Energies is currently edited by Ms. Agatha Cao

More articles in Energies from MDPI
Bibliographic data for series maintained by MDPI Indexing Manager ().