Evolutionary prediction of photovoltaic power plant energy production
Proceedings of the 14th annual conference companion on Genetic and evolutionary computation
Photovoltaic power plant output estimation by neural networks and fuzzy inference
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
Prediction of dental milling time-error by flexible neural trees and fuzzy rules
IDEAL'12 Proceedings of the 13th international conference on Intelligent Data Engineering and Automated Learning
Value estimation of the use case parameters using SOM and fuzzy rules
Proceedings of the International Conference on Management of Emergent Digital EcoSystems
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Fuzzy sets and fuzzy logic can be used for efficient data mining, classification, and value prediction. We propose a genetically evolved fuzzy predictor to estimate the output of a Photovoltaic Power Plant. Photovoltaic Power Plants (PVPPs) are classified as power energy sources with unstable supply of electrical energy. It is necessary to back up power energy from PVPPs for stable electric network operation. An optimal value of back up power can be set with reliable prediction models and significantly contribute to the robustness of the electric network and therefore help in the building of intelligent power grids.