Artificially evolved soft computing models for photovoltaic power plant output estimation

Renewable energy sources are becoming a significant part of todays energy mix. The unstable production of many renewable energy sources including photovoltaic and wind power plants puts increased demands on power transmission systems and on the power grid as a whole. Soft computing methods can contribute to the prediction of electric energy production of renewable resources and therefore to the reliability of the power transmission networks. This work compares two soft computing methods that utilize genetic programming to evolve predictors of a selected renewable energy resource that meets the real world criterion of high output variance and relatively large installed power (in context of the power distribution system of the Czech Republic).

[1]  Václav Snásel,et al.  Towards intrusion detection by information retrieval and genetic programming , 2010, 2010 Sixth International Conference on Information Assurance and Security.

[2]  Andries P. Engelbrecht,et al.  Computational Intelligence: An Introduction , 2002 .

[3]  Stanislav Misak,et al.  Optimizing the mathematical model for prediction of energy production in wind power plants , 2011 .

[4]  Václav Snásel,et al.  The Evolution of Fuzzy Classifier for Data Mining with Applications , 2010, SEAL.

[5]  Jiwen Dong,et al.  Evolving Flexible Neural Networks Using Ant Programming and PSO Algorithm , 2004, ISNN.

[6]  Dorothea Heiss-Czedik,et al.  An Introduction to Genetic Algorithms. , 1997, Artificial Life.

[7]  Tadeusz Sikora,et al.  On Wind Power Station Production Prediction , 2010, NDT.

[8]  Jiwen Dong,et al.  Nonlinear System Modelling Via Optimal Design Of Neural Trees , 2004, Int. J. Neural Syst..

[9]  Václav Snásel,et al.  Fuzzy classification by evolutionary algorithms , 2011, 2011 IEEE International Conference on Systems, Man, and Cybernetics.

[10]  Václav Snásel,et al.  Genetically Evolved Fuzzy Predictor for Photovoltaic Power Output Estimation , 2011, 2011 Third International Conference on Intelligent Networking and Collaborative Systems.

[11]  Peter M. Todd,et al.  Designing Neural Networks using Genetic Algorithms , 1989, ICGA.

[12]  Jiwen Dong,et al.  Time-series forecasting using flexible neural tree model , 2005, Inf. Sci..

[13]  Lawrence Davis,et al.  Training Feedforward Neural Networks Using Genetic Algorithms , 1989, IJCAI.