Short-term and regionalized photovoltaic power forecasting, enhanced by reference systems, on the example of Luxembourg

Daniel Koster, Frank Minette, Christian Braun and Oliver O'Nagy

Renewable Energy, 2019, vol. 132, issue C, 455-470

Abstract: The authors developed a forecasting model for Luxembourg, able to predict the expected regional PV power up to 72 h ahead. The model works with solar irradiance forecasts, based on numerical weather predictions in hourly resolution. Using a set of physical equations, the algorithm is able to predict the expected hourly power production for PV systems in Luxembourg, as well as for a set of 23 chosen PV-systems which are used as reference systems. Comparing the calculated forecasts for the 23 reference systems to their measured power over a period of 2 years, revealed a comparably high accuracy of the forecast. The mean deviation (bias) of the forecast was 1.1% of the nominal power – a relatively low bias indicating low systemic error. The root mean square error (RMSE), lies around 7.4% - a low value for single site forecasts. Two approaches were tested in order to adapt the short-term forecast, based on the present forecast deviations for the reference systems. Thereby, it was possible to improve the very short term forecast on the time horizon of 1–3 h ahead, specifically for the remaining bias, but also systemic deviations can be identified and partially corrected (e.g. snow cover).

Keywords: Photovoltaic forecasting; Forecasting performance; RMSE; Photovoltaic integration; Solar forecasting; Solar energy integration (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (17)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:132:y:2019:i:c:p:455-470

DOI: 10.1016/j.renene.2018.08.005

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