Publication | Peer reviewed papers | Potentiale, Bioenergiesysteme, Logistik
An adaptive short-term forecasting method for the energy yield of flat-plate solar collector systems
Viktor Unterberger, Klaus Lichtenegger, Valentin Kaisermayer, Markus Gölles, Martin Horn
Citation: Unterberger V, Lichtenegger K, Kaisermayer V, Gölles M, Horn M. An adaptive short-term forecasting method for the energy yield of flat-plate solar collector systems. Applied Energy. 2021 Apr 16;2021(293). https://doi.org/10.1016/j.apenergy.2021.116891
The number of large-scale solar thermal installations has increased rapidly in Europe in recent years, with 70 % of these systems operating with flat-plate solar collectors. Since these systems cannot be easily switched on and off but directly depend on the solar radiation, they have to be combined with other technologies or integrated in large energy systems. In order to most efficiently integrate and operate solar systems, it is of great importance to consider their expected energy yield to better schedule heat production, storage and distribution. To do so the availability of accurate forecasting methods for the future solar energy yield are essential. Currently available forecasting methods do not meet three important practical requirements: simple implementation, automatic adaption to seasonal changes and wide applicability. For these reasons, a simple and adaptive forecasting method is presented in this paper, which allows to accurately forecast the solar heat production of flat-plate collector systems considering weather forecasts. The method is based on a modified collector efficiency model where the parameters are continuously redetermined to specifically consider the influence of the time of the day. In order to show the wide applicability the method is extensively tested with measurement data of various flat-plate collector systems covering different applications (below 200 Celsius), sizes and orientations. The results show that the method can forecast the solar yield very accurately with a Mean Absolute Range Normalized Error (MARNE) of about 5 % using real weather forecasts as inputs and outperforms common forecasting methods by being nearly twice as accurate.