The DOI matches its Crossref record.
Regional PV power prediction for improved grid integration
Abstract
The contribution of power production from PV systems to the electricity supply is constantly increasing. An efficient use of the fluctuating solar power production will highly benefit from forecast information on the expected power production, as a basis for management of the electricity grids and trading on the energy market. We present and evaluate the regional PV power prediction system of University of Oldenburg and Meteocontrol GmbH providing forecasts of up to 2 days ahead with hourly resolution. The proposed approach is based on forecasts of the global model of the European Centre for Medium‐Range Forecasts (ECMWF). It includes a post‐processing procedure to derive optimised, site‐specific irradiance forecasts and explicit physical modelling steps to convert the predicted irradiances to PV power. Finally, regional power forecasts are derived by up‐scaling from a representative set of PV systems. The investigation of proper up‐scaling is a special focus of this paper. We introduce a modified up‐scaling approach, modelling the spatial distribution of the nominal power with a resolution of 1° × 1°. The operational PV power prediction system is evaluated in comparison to the modified up‐scaling approach for the control areas of the two German transmission system operators ‘transpower’ and ‘50 Hertz’ for the period 2.7.2009–30.4.2010. rmse values of the operational forecasts are in the range of 4–5% with respect to the nominal power for intra‐day and day‐ahead forecast horizons. Further improvement is achieved with the modified up‐scaling approach. Copyright © 2010 John Wiley & Sons, Ltd.
Overview
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Integrity signals
Registry
Cites
Statistics
Verification
Claims the paper makes
The claims and statistics of this paper haven’t been checked yet.
Run the check — free, no account needed. It reads the open-access text and checks the sources it cites.
Self-check
Percentages, sums and reported differences re-computed from the paper’s own numbers.
The self-check needs the paper’s full text.
Citations
Cited by
Ahmed, R., Sreeram, V., Mishra, Y., & Arif, M. D. (2020) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2020.109792 · Cited by 1,259
Probabilistic energy forecasting: Global Energy Forecasting Competition 2014 and beyond
Hong, T., Pinson, P., Fan, S., Zareipour, H., Troccoli, A., & Hyndman, R. J. (2016) · International Journal of Forecasting · doi:10.1016/j.ijforecast.2016.02.001 · Cited by 1,044
On recent advances in PV output power forecast
Raza, M. Q., Nadarajah, M., & Ekanayake, C. (2016) · Solar Energy · doi:10.1016/j.solener.2016.06.073 · Cited by 544
Markovics, D., & Mayer, M. J. (2022) · Renewable and Sustainable Energy Reviews · doi:10.1016/j.rser.2022.112364 · Cited by 490
Extensive comparison of physical models for photovoltaic power forecasting
Mayer, M. J., & Gróf, G. (2021) · Applied Energy · doi:10.1016/j.apenergy.2020.116239 · Cited by 466
Metadata
- Authors
- Lorenz, E., Scheidsteger, T., Hurka, J., Heinemann, D., & Kurz, C.
- Venue
- Progress in Photovoltaics: Research and Applications
- Publisher
- Wiley
- Published
- 2010-09-08
- Volume
- 19
- Issue
- 7
- Pages
- 757-771
- ISSN
- 1062-7995 (ISSN-L)
- 1099-159X
- OpenAlex
- W2123235979
- Cited by
- 292
- References
- 0
- FWCI
- 7.82
- Type
- journal article
- Language
- en
- Open access
- closed
About these checks
Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.
Statistics and arithmetic are recomputed, not judged.
Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.
What counts as a claim ›How we measure confidence ›How we check this ›
Are you an author, or do you see a problem with this page? Request a review
How to cite
Cite CoChat for the checks on this page, and the paper for its own findings.
- This page
- CoChat. (2026). Citation check: Regional PV power prediction for improved grid integration. https://app.cochat.ai/papers/10.1002/pip.1033
- The paper
- Lorenz, E., Scheidsteger, T., Hurka, J., Heinemann, D., & Kurz, C. (2011). Regional PV power prediction for improved grid integration. Progress in Photovoltaics: Research and Applications. https://doi.org/10.1002/pip.1033
Data sources
Bibliographic data: Semantic Scholar, OpenAlex, Crossref.
Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).
Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.
