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Prediction of Wind Power by Chaos and BP Artificial Neural Networks Approach Based on Genetic Algorithm
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 Title & Authors
Prediction of Wind Power by Chaos and BP Artificial Neural Networks Approach Based on Genetic Algorithm
Huang, Dai-Zheng; Gong, Ren-Xi; Gong, Shu;
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It is very important to make accurate forecast of wind power because of its indispensable requirement for power system stable operation. The research is to predict wind power by chaos and BP artificial neural networks (CBPANNs) method based on genetic algorithm, and to evaluate feasibility of the method of predicting wind power. A description of the method is performed. Firstly, a calculation of the largest Lyapunov exponent of the time series of wind power and a judgment of whether wind power has chaotic behavior are made. Secondly, phase space of the time series is reconstructed. Finally, the prediction model is constructed based on the best embedding dimension and best delay time to approximate the uncertain function by which the wind power is forecasted. And then an optimization of the weights and thresholds of the model is conducted by genetic algorithm (GA). And a simulation of the method and an evaluation of its effectiveness are performed. The results show that the proposed method has more accuracy than that of BP artificial neural networks (BP-ANNs).
Wind power forecasting;Chaos and BP neural network method;Genetic algorithm;
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Journal of Electrical Engineering and Technology, 2015. vol.10. 3, pp.832-837 crossref(new window)
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Ramesh Babu N and Arulmozhivarman P, “Improving forecast accuracy of wind speed using wavelet transform and neural networks,” J Electr Eng Technol, vol. 8, pp. 559-564, 2013. crossref(new window)

Ramesh Babu N and Arulmozhivarman P, “Forecasting of wind speed using artificial neural networks,” Int. Rev. Mod. Sim, vol.5, no.5, 2012.

Jae-Kun Lyu, Jae-Haeng Heo, Mun-Kyeom Kim and Jong-Keun Park, “Impacts of wind power integration on generation dispatch in power systems,” J Electr Eng Technol, vol.8, pp.453-463, 2013. crossref(new window)

Ch. Ulam-Orgil, Hye-Won Lee and Yong-Cheol Kang, “Evaluation of the wind power penetration limit and wind energy penetration in the Mongolian central power system,” J Electr Eng Technol, vol. 7, pp. 852-858, 2012. crossref(new window)

Poncela Marta, Poncela Pilar and Ramon Peran Jose, “Automatic tuning of Kalman filters by maximum likelihood methods for wind energy forecasting,” Appl. Energy, vol. 108, pp. 349-362,2013. crossref(new window)

Kou Peng, Gao Feng and Guan Xiaohong, “Sparse online warped Gaussian process for wind power probabilistic forecasting,” Appl. Energy, vol. 108, pp. 410-428, 2013. crossref(new window)

Zhang Wenyu, Wang Jujie and Wang Jianzhou, “Short-term wind speed forecasting based on a hybrid model,” Appl. Soft Comput, vol. 13, pp. 3225-3233, 2013. crossref(new window)

Zhou Z, Botterud A and Wang J, “Application of probabilistic wind power forecasting in electricity markets,” Wind Energy, vol. 16, pp. 321-338, 2013. crossref(new window)

Rasoolzadeh Arsalan and Tavazoei Mohammad Saleh, “Prediction of chaos in non-salient permanent-magnet synchronous machines,” Phys Lett A, vol. 33, pp. 73-79, 2012.

Farzin S, Ifaei P and Farzin N, “An investigation on changes and prediction of Urmia Lake water surface evaporation by chaos theory,” Int J Environ Res, Vol. 6, pp. 815-824, 2012.

PaoH siao-Tien, “Forecasting electricity market pricing using artificial neural networks,” Energ Convers Manage, vol. 48, pp. 907-912, 2007. crossref(new window)

Ozgur Tayfun, Tuccar Gokhan and Ozcanli Mustafa, “Prediction of emissions of a diesel engine fueled with soybean biodiesel using artificial neural networks,” Energy Education Science And T, vol. 27, pp. 301-312, 2011.

Grossi Enzo and Buscema Massimo, “Introduction to artificial neural networks,” Eur J Gastroen Hepat, vol. 19, pp. 1046-1054, 2007. crossref(new window)

Wolf A, “Determining Lyapunov exponents from a time series,” Physica D, vol. 16, pp. 285-317, 1985. crossref(new window)

Grassberger P, “Generalized dimensions of strange attractors,” Phys Lett A, vol. 97, pp. 227-230, 1983. crossref(new window)

Kim H S, Eykholt R and Salas J D, “Nonlinear dynamics, delay times and embedding windows,” Physica D, vol. 127, pp. 48-60, 1999. crossref(new window)