Title :
Short term power prediction of the photovoltaic power station based on comparison of power profile sequences using F-Score computation
Author :
Radvansky, M. ; Kudelka, M. ; Snasel, V.
Author_Institution :
Dept. of Comput. Sci., VrB - Tech. Univ. of Ostrava, Poruba, Czech Republic
Abstract :
Due to the annual increase in energy prices, photovoltaic power stations (PVPS) are often used as a primary source of power for smart off-grid houses. Integration of this kind of energy source is challenging because it is a source of variably generated power due to meteorological uncertainty, but the cost of this energy source rapidly decreases. In this paper, we present results of the short term prediction method of generated power for small PVPS based on self-organizing maps, previously introduced power profiles, their sequences and computing F-Score as an alternative to commonly used algorithms.
Keywords :
building integrated photovoltaics; prediction theory; pricing; self-adjusting systems; smart power grids; F-score computation; energy prices; energy source cost; meteorological uncertainty; photovoltaic power station; power primary source; power profile sequence comparison; short term power prediction method; small PVPS based on self-organizing maps; smart off-grid houses; variably generated power; Meteorology; Neurons; Photovoltaic systems; Power measurement; Vectors; photovoltaic power station; power profile; prediction;
Conference_Titel :
Systems, Man and Cybernetics (SMC), 2014 IEEE International Conference on
Conference_Location :
San Diego, CA
DOI :
10.1109/SMC.2014.6974471