Online short-term solar power forecasting

Peder Bacher, Henrik Madsen, Henrik Aalborg Nielsen

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    This paper describes a new approach to online forecasting of power production from PV systems. The method is suited to online forecasting in many applications and in this paper it is used to predict hourly values of solar power for horizons of up to 36 hours. The data used is fifteen-minute observations of solar power from 21 PV systems located on rooftops in a small village in Denmark. The suggested method is a two-stage method where first a statistical normalization of the solar power is obtained using a clear sky model. The clear sky model is found using statistical smoothing techniques. Then forecasts of the normalized solar power are calculated using adaptive linear time series models. Both autoregressive (AR) and AR with exogenous input (ARX) models are evaluated, where the latter takes numerical weather predictions (NWPs) as input. The results indicate that for forecasts up to two hours ahead the most important input is the available observations of solar power, while for longer horizons NWPs are the most important input. A root mean square error improvement of around 35 % is achieved by the ARX model compared to a proposed reference model.
    Original languageEnglish
    JournalSolar Energy
    Issue number10
    Pages (from-to)1772-1783
    Publication statusPublished - 2009


    • photovoltaic
    • time series
    • quantile regression
    • forecasting
    • Solar power
    • prediction
    • clear sky model
    • recursive least squares
    • numerical weather predictions


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