Probabilistic Intraday PV Power Forecast Using Ensembles of Deep Gaussian Mixture Density Networks

Oliver Doelle (), Nico Klinkenberg, Arvid Amthor and Christoph Ament
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Oliver Doelle: Siemens AG, Digital Industries—Data Visions, Siemenspromenade 1, 91058 Erlangen, Germany
Nico Klinkenberg: Faculty of Business Studies and Information Technology, Westphalian University of Applied Sciences, 46397 Bocholt, Germany
Arvid Amthor: Siemens AG, Technology, Günther-Scharowsky-Str. 1, 91058 Erlangen, Germany
Christoph Ament: Faculty of Applied Computer Science, University of Augsburg, 86159 Augsburg, Germany

Energies, 2023, vol. 16, issue 2, 1-17

Abstract: There is a growing interest of estimating the inherent uncertainty of photovoltaic (PV) power forecasts with probability forecasting methods to mitigate accompanying risks for system operators. This study aims to advance the field of probabilistic PV power forecast by introducing and extending deep Gaussian mixture density networks (MDNs). Using the sum of the weighted negative log likelihood of multiple Gaussian distributions as a minimizing objective, MDNs can estimate flexible uncertainty distributions with nearly all neural network structures. Thus, the advantages of advances in machine learning, in this case deep neural networks, can be exploited. To account for the epistemic (e.g., model) uncertainty as well, this study applies two ensemble approaches to MDNs. This is particularly relevant for industrial applications, as there is often no extensive (manual) adjustment of the forecast model structure for each site, and only a limited amount of training data are available during commissioning. The results of this study suggest that already seven days of training data are sufficient to generate significant improvements of 23.9% in forecasting quality measured by normalized continuous ranked probability score (NCRPS) compared to the reference case. Furthermore, the use of multiple Gaussian distributions and ensembles increases the forecast quality relatively by up to 20.5% and 19.5%, respectively.

Keywords: PV power; probabilistic forecast; MDN; Monte Carlo dropout; deep ensemble (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: 2023
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Citations: View citations in EconPapers (2)

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