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.
Original language | English |
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Journal | Solar Energy |
Volume | 83 |
Issue number | 10 |
Pages (from-to) | 1772-1783 |
ISSN | 0038-092X |
DOIs | |
Publication status | Published - 2009 |
Keywords
- photovoltaic
- time series
- quantile regression
- forecasting
- Solar power
- prediction
- clear sky model
- recursive least squares
- numerical weather predictions