DOI QR코드

DOI QR Code

Prediction of Wind Power by Chaos and BP Artificial Neural Networks Approach Based on Genetic Algorithm

Huang, Dai-Zheng;Gong, Ren-Xi;Gong, Shu

  • Received : 2013.08.25
  • Accepted : 2014.09.04
  • Published : 2015.01.01

Abstract

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).

Keywords

Wind power forecasting;Chaos and BP neural network method;Genetic algorithm

References

  1. Kim H S, Eykholt R and Salas J D, “Nonlinear dynamics, delay times and embedding windows,” Physica D, vol. 127, pp. 48-60, 1999. https://doi.org/10.1016/S0167-2789(98)00240-1
  2. 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.
  3. Grossi Enzo and Buscema Massimo, “Introduction to artificial neural networks,” Eur J Gastroen Hepat, vol. 19, pp. 1046-1054, 2007. https://doi.org/10.1097/MEG.0b013e3282f198a0
  4. Wolf A, “Determining Lyapunov exponents from a time series,” Physica D, vol. 16, pp. 285-317, 1985. https://doi.org/10.1016/0167-2789(85)90011-9
  5. Grassberger P, “Generalized dimensions of strange attractors,” Phys Lett A, vol. 97, pp. 227-230, 1983. https://doi.org/10.1016/0375-9601(83)90753-3
  6. 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. https://doi.org/10.1016/j.apenergy.2013.03.041
  7. PaoH siao-Tien, “Forecasting electricity market pricing using artificial neural networks,” Energ Convers Manage, vol. 48, pp. 907-912, 2007. https://doi.org/10.1016/j.enconman.2006.08.016
  8. 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. https://doi.org/10.5370/JEET.2013.8.3.453
  9. 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. https://doi.org/10.5370/JEET.2012.7.6.852
  10. 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. https://doi.org/10.1016/j.apenergy.2013.03.038
  11. 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. https://doi.org/10.1016/j.asoc.2013.02.016
  12. Ramesh Babu N and Arulmozhivarman P, “Forecasting of wind speed using artificial neural networks,” Int. Rev. Mod. Sim, vol.5, no.5, 2012.
  13. Zhou Z, Botterud A and Wang J, “Application of probabilistic wind power forecasting in electricity markets,” Wind Energy, vol. 16, pp. 321-338, 2013. https://doi.org/10.1002/we.1496
  14. 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.
  15. 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.
  16. 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. https://doi.org/10.5370/JEET.2013.8.3.559

Cited by

  1. Wind power forecasting approach using neuro-fuzzy system combined with wavelet packet decomposition, data preprocessing, and forecast combination framework vol.41, pp.4, 2017, https://doi.org/10.1177/0309524X17709726
  2. A Multi Time Scale Wind Power Forecasting Model of a Chaotic Echo State Network Based on a Hybrid Algorithm of Particle Swarm Optimization and Tabu Search vol.8, pp.12, 2015, https://doi.org/10.3390/en81112317
  3. Wind Power Generation Forecasting Using Least Squares Support Vector Machine Combined with Ensemble Empirical Mode Decomposition, Principal Component Analysis and a Bat Algorithm vol.9, pp.12, 2016, https://doi.org/10.3390/en9040261
  4. Comparisons of forecasting for hepatitis in Guangxi Province, China by using three neural networks models vol.4, 2016, https://doi.org/10.7717/peerj.2684
  5. MULTIFRACTAL BEHAVIOR OF WIND SPEED AND WIND DIRECTION vol.24, pp.01, 2016, https://doi.org/10.1142/S0218348X16500031
  6. Forecasting outpatient visits using empirical mode decomposition coupled with back-propagation artificial neural networks optimized by particle swarm optimization vol.12, pp.2, 2017, https://doi.org/10.1371/journal.pone.0172539
  7. A combined multivariate model for wind power prediction vol.144, 2017, https://doi.org/10.1016/j.enconman.2017.04.077
  8. Daily Peak Load Forecasting Based on Complete Ensemble Empirical Mode Decomposition with Adaptive Noise and Support Vector Machine Optimized by Modified Grey Wolf Optimization Algorithm vol.11, pp.1, 2018, https://doi.org/10.3390/en11010163
  9. Intelligent Forecasting Model for Regional Power Grid With Distributed Generation vol.11, pp.3, 2017, https://doi.org/10.1109/JSYST.2015.2438315
  10. Forecasting Chaotic Time Series Via Anfis Supported by Vortex Optimization Algorithm: Applications on Electroencephalogram Time Series vol.42, pp.8, 2017, https://doi.org/10.1007/s13369-016-2279-z
  11. Application of a Hybrid Method Combining Grey Model and Back Propagation Artificial Neural Networks to Forecast Hepatitis B in China vol.2015, 2015, https://doi.org/10.1155/2015/328273
  12. Censored spatial wind power prediction with random effects vol.51, 2015, https://doi.org/10.1016/j.rser.2015.06.047
  13. Forecasting of Energy-Related CO2 Emissions in China Based on GM(1,1) and Least Squares Support Vector Machine Optimized by Modified Shuffled Frog Leaping Algorithm for Sustainability vol.10, pp.4, 2018, https://doi.org/10.3390/su10040958
  14. Forecast of Chaotic Series in a Horizon Superior to the Inverse of the Maximum Lyapunov Exponent vol.2018, pp.1099-0526, 2018, https://doi.org/10.1155/2018/1452683
  15. An Ant-Lion Optimizer-Trained Artificial Neural Network System for Chaotic Electroencephalogram (EEG) Prediction vol.8, pp.9, 2018, https://doi.org/10.3390/app8091613
  16. Design of Phase Gradient Coding Metasurfaces for Broadband Wave Modulating vol.8, pp.1, 2018, https://doi.org/10.1038/s41598-018-26981-6
  17. Forecasting of Power Grid Investment in China Based on Support Vector Machine Optimized by Differential Evolution Algorithm and Grey Wolf Optimization Algorithm vol.8, pp.4, 2018, https://doi.org/10.3390/app8040636
  18. Ultra-Short-Term Wind-Power Forecasting Based on the Weighted Random Forest Optimized by the Niche Immune Lion Algorithm vol.11, pp.5, 2018, https://doi.org/10.3390/en11051098