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Article

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

1
CSEM PV-Center, Rue Jaquet-Droz 1, 2000 Neuchâtel, Switzerland
2
BKW AG, Viktoriaplatz 2, 3013 Bern, Switzerland
*
Author to whom correspondence should be addressed.
Energies 2020, 13(21), 5763; https://doi.org/10.3390/en13215763
Received: 5 October 2020 / Revised: 22 October 2020 / Accepted: 27 October 2020 / Published: 3 November 2020
(This article belongs to the Special Issue Smart Photovoltaic Energy Systems for a Sustainable Future)
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. View Full-Text
Keywords: multi-site photovoltaic forecasting; spatio-temporal correlation; graph signal processing; signal reconstruction multi-site photovoltaic forecasting; spatio-temporal correlation; graph signal processing; signal reconstruction
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MDPI and ACS Style

Carrillo, R.E.; Leblanc, M.; Schubnel, B.; Langou, R.; Topfel, C.; Alet, P.-J. High-Resolution PV Forecasting from Imperfect Data: A Graph-Based Solution. Energies 2020, 13, 5763. https://doi.org/10.3390/en13215763

AMA Style

Carrillo RE, Leblanc M, Schubnel B, Langou R, Topfel C, Alet P-J. High-Resolution PV Forecasting from Imperfect Data: A Graph-Based Solution. Energies. 2020; 13(21):5763. https://doi.org/10.3390/en13215763

Chicago/Turabian Style

Carrillo, Rafael E., Martin Leblanc, Baptiste Schubnel, Renaud Langou, Cyril Topfel, and Pierre-Jean Alet. 2020. "High-Resolution PV Forecasting from Imperfect Data: A Graph-Based Solution" Energies 13, no. 21: 5763. https://doi.org/10.3390/en13215763

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