Photovoltaic power plant power output prediction using fuzzy rules

• Autorzy: Prokop, L. , Misak, S. , Snášel, V. , Krömer, P. , Platos, J.

Warianty tytułu

PL

Przewidywanie mocy wyjściowej systemu fotowoltaicznego z wykorzystaniem logiki rozmytej

• Języki publikacji: EN

Abstrakty

EN

Photovoltaic Power Plants (PVPP) are classified as a power energy sources with non-stabile supply of electric energy. It is necessary to back up power energy from PVPP for stabile electric network operation. We can set an optimal value of back up power energy with using variety of prediction models and methods for PVPP Power output prediction. Fuzzy classifiers and fuzzy rules can be informally defined as tools that use fuzzy sets or fuzzy logic for their operations. In this paper, we use genetic programming to evolve a fuzzy classifier in the form of a fuzzy search expression to predict PVPP Power output.

PL

Opisano różne metody przewidywania możliwości system fotowoltaicznego. Jedną z metod jest logika rozmyta – fuzzy logic. Opisano programowanie genetyczne do tworzenia rozmytego klasyfikatora.

Słowa kluczowe

PL

system fotowoltaiczny; logika rozmyta; przewidywanie

EN

photovoltaic power plant; fuzzy rule; prediction

• Czasopismo: Przegląd Elektrotechniczny
• Rocznik: 2013
• Tom: R. 89, nr 11
• Strony: 77-80
• Opis fizyczny: Bibliogr. 7 poz., rys., tab.

Twórcy

autor

Prokop, L.

• VSB-Technical University of Ostrava,

autor

Misak, S.

• VSB-Technical University of Ostrava,

autor

Snášel, V.

• VSB - Technical University of Ostrava,

autor

Krömer, P.

• VSB - Technical University of Ostrava,

autor

Platos, J.

• VSB - Technical University of Ostrava,

Bibliografia

• [1] J. Koza, “Genetic programming: A paradigm for genetically breeding populations of computer programs to solve problems,” Technical Report STAN-CS-90-1314, Dept. of Computer Science, Stanford University, 1990.
• [2] J. R. Koza, Genetic Programming: On the Programming of Computers by Means of Natural Selection. Cambridge, MA, USA: MIT Press, 1992.
• [3] M. Affenzeller, S. Winkler, S. Wagner, and A. Beham, Genetic Algorithms and Genetic Programming: Modern Concepts and Practical Applications. Chapman & Hall/CRC, 2009.
• [4] V. Snásel, P. Krömer, J. Platos, and A. Abraham, “The evolution of fuzzy classifier for data mining with applications,” in SEAL (K. Deb, A. Bhattacharya, N. Chakraborti, P. Chakroborty, S. Das, J. Dutta, S. K. Gupta, A. Jain, V. Aggarwal, J. Branke, S. J. Louis, and K. C. Tan, eds.), vol. 6457 of Lecture Notes in Computer Science, pp. 349–358, Springer, 2010.
• [5] PROKOP, L., MISAK, S., SIKORA, T., DASAL, K., POPłAWSKI, T., RUSEK, B. Optimizing Electric Network Running with Wind Power Plant by using Production Prediction Mathematical Models. Rynek Energii. 2009, vol. II, no.3, p.313-318.
• [6] MISAK, Stanislav; PROKOP, Lukas: Prediction System for Energy Production from WPP. In proceedings of 11th International Scientific Conference Electric Power Engineering 2010; (IEEE EPE 2010), 2010, Brno:VUT Brno, ISBN 978-80- 214-4094-4, pg.301-305
• [7] P. Bacher, M. Madsen, and H. A. Nielsen, “Online short-term solar energy forecasting,” Solar Energy, vol. 83, pp. 1772-1783, 2009.