||Short-term Solar Power Forecasting
||Bacher, Peder (Mathematical Statistics, Department of Informatics and Mathematical Modeling, Technical University of Denmark, DTU, DK-2800 Kgs. Lyngby, Denmark)
||Madsen, Henrik (Mathematical Statistics, Department of Informatics and Mathematical Modeling, Technical University of Denmark, DTU, DK-2800 Kgs. Lyngby, Denmark)
||Technical University of Denmark, DTU, DK-2800 Kgs. Lyngby, Denmark
||The share of the global power production coming from solar power is increasing. Forecasts of solar power is a key point for a successful integration of the solar
power production into the existing electricity grid. In the present thesis a solar
power forecasting method is developed.
The overall production of 21 grid-connected photovoltaic (PV) systems with
peak power in the range of 1 kWp to 4 kWp is used. The PV systems are
located within an area of a few square kilometers. Numerical weather predictions
(NWP) of global irradiation from the mesoscale NWP model DMI-Hirlam, is
also used as input to the method.
A clear sky model only based on solar power observations is developed. It is used
to transform the solar power process into a stationary process that resembles the
transmittance of the atmosphere. This process is modeled with linear models
and the best model both with and without NWPs as input is identi¯ed.
Adaptive estimation is found to be a requisite. Therefore the prediction models
of the transformed solar power process are fitted using k-step recursive least
squares with forgetting.
The evaluation focus on solar power forecasts for the purpose of bidding into
the electricity market Elspot. The forecasts are issued at 12:00 UTC each day
and consist of hour value predictions up to a 36 hour horizon of solar power.
These forecasts are evaluated and compared to a persistence reference model.
The achieved results clearly indicate an increasing performance for next day
horizons (12 to 36 hours), by the model using NWPs as input. Whereas for
very short-term predictions (less than 6 hours) the solar power observations are
the most important input.
Finally ideas for both refinements and extensions to the method are outlined, together with a suggestion for the development of a framework for standardization
of solar power forecasting method evaluation.
Creation date: 2008-02-19
Update date: 2009-11-24