Abstract
Different meteorological data series called multiyear data, long-term average measured data series, or test reference years (TRYs) are required for solar energy system simulation. It is known that the use of the multiyear data approach requires a great effort in time and computation, long-term average measured data do not have the extreme values of weather data given along the year, and TRYs represent typical references rather than extreme conditions and facilitate the comparison in the performance of energy systems. In this paper, TRYs have been generated, using three different methodologies, from hourly meteorological data measured in two cities, Madrid and Valladolid (Spain). In order to evaluate them, the performance simulation of three solar energy systems (thermal, passive, and photovoltaic) with long-term measured meteorological data has been compared with estimated performance simulations with TRYs. Root-mean-square and mean bias errors and relative differences have been used as estimators to measure the performance deviation of TRYs from long-term measured meteorological data series. Results of the comparison show that the most appropriate method for generating test reference year depends on the characteristics of the station and varies from month to month. The Danish method (TRY5) gives better results in Valladolid than in Madrid for the photovoltaic and passive systems; the Argirious method (TRY6) gives better results in Madrid than in Valladolid for the photovoltaic system. The Pissimanis method (TRY4) is the best for simulating thermal and photovoltaic systems in summer.
Corresponding author address: Dr. Julia Bilbao, Department of Applied Physics, University of Valladolid, 47005 Valladolid, Spain. juliab@fal.uva.es
