Programm Photovoltaik Ausgabe 2008 ... - Bundesamt für Energie BFE

Introduction
In the framework of IEA Solar Heating and Cooling (SHC) Task 36 “Solar Resource Knowledge Management”
Meteotest investigates mainly the possibilities and quality of global radiation forecast.
The Task 36 is divided into 3 main subtasks:
a) Standard qualification for solar resource products (includes benchmarking of different radiation
estimation models based on satellite measurements).
b) Common structure for archiving and accessing solar resource products (includes prototype of
online tool for accessing data).
c) Improved techniques for solar resource characterization and forecast; improve satellite retrieval
methods for solar radiation products; conduct climatological analysis of solar resources.
The aim in the radiation forecast subtask is to define the quality of the existing models and to enhance
the quality. A further aim is also to check the quality of forecasted PV production data. Main partners
in this subtask are University Oldenburg and New York State University at Albany.
Additionally Meteotest did some work within part c), where a new turbidity climatology was made.
Work done and first results
Radiation forecast
Meteotest made a short analysis of the operational global radiation forecast of the MM5 model, which
runs with input data of GFS (global model of US National Weather Service) and in spatial resolution of
30 km.
The global radiation was used in form of direct model output. We tested the hourly values at 4 different
stations in Switzerland (Basel, Geneva, Lugano and Schaffhausen). The validation was made with
measured values from the network of MeteoSwiss.
The validation forecast ranged from 9 – 63 hours (9, 12 15 h for day 1 - 3)
The test period was June 16th 2006 – October 16th 2006 (1.33 years)
The validation showed the following results:
1. Mean biases are small
2. relative root mean squared errors are (prmse):
All seasons, day 1, all hours: 53%; summer, day 1, 12 UTC: 38%
3. The quality (prmse) is: dependent on time of day (factor 1.3); strongly dependent on season
(factor 2)
The ongoing validations of the subtask members showed partly different results. The groups working
with MM5 models got similar results in Spain.
New York State University at Albany (Richard Perez) used digital weather forecast of cloud cover of
US National Weather Service. Those results showed a Prediction Root Mean Squared Error of
20-40% depending on the region (Desert Rock, Boulder, Goodwin Creek).
University of Oldenburg used ECMWF (European weather model). Those results showed a Prediction
Root Mean Squared Error of 30-37% depending on the region (Germany and Spain). This could be
partly due to a better radiation model within the ECMWF model.
As quality is very much depending on site and time of the year, the team members will conduct next
year validations for the same regions and periods for all models.
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Solar Resource Management, J. Remund, Meteotest
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