Abstract
Power generation from solar and wind energy systems is highly variable due to its dependence on meteorological conditions. With the constantly increasing contribution of photovoltaic (PV) power to the electricity mix, reliable predictions of the expected PV power production are getting more and more important as a basis for management and operation strategies. We give an overview of different approaches for solar irradiance and PV power prediction, including numerical weather predictions for forecast horizons of several days, very short-term forecasts based on the detection of cloud motion in satellite or ground-based sky images, and statistical methods to optimize and combine different data sources as well as methods for PV simulation and upscaling to regional PV power predictions. Evaluation results for selected irradiance and power prediction schemes show the benefit of different approaches for different timescales.
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References
Bacher P, Madsen H, Nielsen HA (2009) Online short-term solar power forecasting. Sol Energy 83:1772–1783
Beyer HG, Luther J, Steinberger-Willms R (1993) Power fluctuations in spatially dispersed wind turbine systems. Solar Energy 50:297–305
Beyer HG, Betcke J, Drews A et al (2004) Identification of a general model for the MPP performance of PV modules for the application in a procedure for the performance check of grid connected systems. In: Proceedings of the 19th European Photovoltaic Solar Energy Conference and Exhibition, pp 3073–3076
Chow CW, Urquhart B, Lave M et al (2011) Intra-hour forecasting with a total sky imager at the UC3 San Diego solar energy testbed. Sol Energy 85(11):2881–2893. doi:10.1016/j.solener.2011.08.025
Dudhia J (1989) Numerical study of convection observed during the winter monsoon experiment using a mesoscale two-dimensional model. J Atmospheric Sci 46:3077–3107. doi:10.1175/1520-0469(1989)0462.0.CO;2
Glahn HR, Lowry DA (1972) The use of model output statistics (MOS) in objective weather forecasting. J Appl Meteorol 11:1203–1211
Hammer A, Heinemann D, Hoyer C et al (2003) Solar energy assessment using remote sensing technologies. Remote Sens Environ 86:423–432
Klucher TM (1979) Evaluation of models to predict insolation on tilted surfaces. Sol Energy 23:111–114. doi:10.1016/S0038-092X(87)80031-2
Kühnert J, Lorenz E, Heinemann D (2013) Satellite-based irradiance and power forecasting for the german energy market' in solar energy forecasting and resource assessment, Editor: Jan Kleissl (Elsevier 2013).
Lara-Fanego V, Ruiz-Arias JA, Pozo-Vázquez D, Santos-Alamillos FJ, Tovar-Pescador J (2012) Evaluation of the WRF model solar irradiance forecasts in Andalusia (Southern Spain). Sol Energy 86(8):2200–2217. doi:10.1016/j.solener.2011.02.014
Lorenz E, Heinemann D (2012) Prediction of solar irradiance and photovoltaic power. Compr Renew Energy 1:239–292. doi:10.1016/B978-0-08-087872-0.00114-1
Lorenz E, Heinemann D, Hammer A (2004) Short-term forecasting of solar radiation based on satellite data. In: Proceedings of EuroSun 2004. Freiburg, Germany, pp 841–848
Lorenz E, Hurka J, Heinemann D, Beyer HG (2009) Irradiance forecasting for the power prediction of grid-connected photovoltaic systems. IEEE J Special Topics Earth Observ Remote Sens 2:2–10
Lorenz E, Scheidsteger T, Hurka J et al (2011) Regional PV power prediction for improved grid integration. Progr Photovoltaics Res Appl 19: 757–771. doi:10.1002/pip.1033
Lorenz E, Kühnert J, Heinemann D (2012) Short term forecasting of solar irradiance by combining satellite data and numerical weather predictions. In: Proceedings of 27th European Photovoltaic Solar Energy Conference, Valencia, Spain, pp 4401–440
Lorenz E, Heinemann D, Kurz C (2012b) Local and regional photovoltaic power prediction for large scale grid integration: assessment of a new algorithm for snow detection. Progr Photovoltaics Res Appl 20:760–769. doi:10.1002/pip.1224
Marquez R, Coimbra CFM (2011) Forecasting of global and direct solar irradiance using stochastic learning methods, ground experiments and the NWS database. Sol Energy 85:746–756
Molteni F, Buizza R, Palmer TN, Petroliagis T (1996) The ECMWF ensemble prediction system: methodology and validation. Quart J Royal Meteorol Soc 122:73–119
Morcrette JJ, Barker HW, Cole JNS et al (2008) Impact of a new radiation package, McRad, in the ECMWF integrated forecasting system. Mon Weather Rev 136:4773–4798
Pedro HTC, Coimbra CFM (2012) Assessment of forecasting techniques for solar power output with no exogenous variables. Sol Energy 86:2017–2028
Pelland S, Gallanis G, Kallos G (2013) Solar and photovoltaic forecasting through post-processing of the global environmental multiscale numerical weather prediction model. Progr Photovoltaics Res Appl 21: 284–296. doi:10.1002/pip.1180
Reikard G (2009) Predicting solar radiation at high resolutions: a comparison of time series forecasts. Sol Energy 83(3):342–349. doi:10.1016/j.solener.2008.08.007
Remund J, Schilter C, Dierer S et al (2008) Operational forecast of PV production. In: Proceedings of 23rd European Photovoltaic Solar Energy Conference, Valencia, Spain, pp 3138–3140
Skamarock WC, Klemp JB, Dudhia J et al (2008) A description of the advanced research WRF version 3. TechnicalNote NCAR/TN-475+STR. Mesoscale and Microscale Meteorology Division, National Center for Atmospheric Research, Boulder
Traunmüller W, Steinmaurer G (2010) Solar irradiance forecasting, benchmarking of different techniques and applications to energy meteorology. In: Proceedings of EuroSun 2010. Graz, Austria
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Lorenz, E., Kühnert, J., Heinemann, D. (2014). Overview of Irradiance and Photovoltaic Power Prediction. In: Troccoli, A., Dubus, L., Haupt, S. (eds) Weather Matters for Energy. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-9221-4_21
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DOI: https://doi.org/10.1007/978-1-4614-9221-4_21
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