Online short-term solar power forecasting

Abstract

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.

A non-parametric method for correction of global radiation observations

“…Considering the fitting of the clear-sky model it is noted that the model which should be applied, is dependent on the time resolution of the data. For resolutions higher than hourly a two-dimensional kernel, which also includes the time of day dimension, should be used, as in (Bacher et al, 2009) where a similar clear-sky model was applied to fifteen minutes values. Regarding the parameters needed to be tuned in the clear-sky model -the quantile, kernel bandwidth, and order of the polynomial -some manual interaction is required.…”

“…It is this feature which enables the model to be used for correction. The statistical clear-sky model is a nonparametric model based on local polynomial quantile regression (Koenker, 2005) similar to the clear-sky model presented in (Bacher et al, 2009).…”

“…A clear-sky model, similar to the one proposed by Bacher et al (2009) for observations of solar power, is here proposed for observations of global radiation. The proposed clear-sky model does not include any prior physical knowledge, it is based solely on the information obtained from the observations.…”

Model-free computation of ultra-short-term prediction intervals of solar irradiance

“…It ranges between 14% and 80% of the point-forecast value. Bacher et al (2009) propose a prediction-intervals computation by using quantile regression. Their proposed method is suitable for online forecasting.…”

“…The available literature on prediction intervals for PV energy-conversion systems is characterized by the following four main limitations (Singh et al, 2013;Kardakos et al, 2013;Trapero et al, 2014;Lorenz et al, 2009;Marquez and Coimbra, 2011;Bacher et al, 2009;Segura and Vercher, 2001): (i) absence of methods proposing prediction intervals targeting the time scale of seconds or sub-seconds; (ii) absence of methods proposing prediction intervals able to track the highly-dynamic volatility of the solar irradiance; (iii) absence of methods able to account for distributions of the point-forecast errors other than Gaussian; (iv) strong dependency of the prediction interval with the specific method used for the point forecast computation. To the best of our knowledge, the only works that are independent of the point-forecast method are (Wan et al, 2014;Pinson and Tastu, 2014).…”