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Mingfei Niu; Shaolong Sun; Jie Wu; Yuanlei Zhang (2015)
Publisher: Hindawi Limited
Journal: Mathematical Problems in Engineering
Languages: English
Types: Article
Subjects: TA1-2040, Mathematics, Engineering (General). Civil engineering (General), Article Subject, QA1-939

Classified by OpenAIRE into

ACM Ref: ComputerApplications_MISCELLANEOUS
arxiv: Physics::Atmospheric and Oceanic Physics, Physics::Space Physics, Astrophysics::High Energy Astrophysical Phenomena, Astrophysics::Solar and Stellar Astrophysics
The accuracy of wind speed forecasting is becoming increasingly important to improve and optimize renewable wind power generation. In particular, reliable short-term wind speed forecasting can enable model predictive control of wind turbines and real-time optimization of wind farm operation. However, due to the strong stochastic nature and dynamic uncertainty of wind speed, the forecasting of wind speed data using different patterns is difficult. This paper proposes a novel combination bias correcting forecasting method, which includes the combination forecasting method and forecasting bias correcting model. The forecasting result shows that the combination bias correcting forecasting method can more accurately forecast the trend of wind speed and has a good robustness.
  • The results below are discovered through our pilot algorithms. Let us know how we are doing!

    • Monfared, M., Rastegar, H., Kojabadi, H. M.. A new strategy for wind speed forecasting using artificial intelligent methods. Renewable Energy. 2009; 34 (3): 845-848
    • D'Amico, G., Petroni, F., Prattico, F.. Wind speed and energy forecasting at different time scales: a nonparametric approach. Physica A. 2014; 406: 59-66
    • Liu, H., Tian, H.-Q., Li, Y.-F.. Comparison of two new ARIMA-ANN and ARIMA-Kalman hybrid methods for wind speed prediction. Applied Energy. 2012; 98: 415-424
    • Tascikaraoglu, A., Uzunoglu, M.. A review of combined approaches for prediction of short-term wind speed and power. Renewable and Sustainable Energy Reviews. 2014; 34: 243-254
    • Song, Z., Jiang, Y., Zhang, Z.. Short-term wind speed forecasting with Markov-switching model. Applied Energy. 2014; 130: 103-112
    • Zhou, M., Yan, Z., Ni, Y. X.. A novel ARIMA approach on electricity price forecasting with the improvement of predicted error. Proceeding of the CSEE. 2004; 24 (12): 63-68
    • Aghaei, J., Niknam, T., Azizipanah-Abarghooee, R., Arroyo, J. M.. Scenario-based dynamic economic emission dispatch considering load and wind power uncertainties. International Journal of Electrical Power and Energy Systems. 2013; 47 (1): 351-367
    • Jiang, Y., Song, Z., Kusiak, A.. Very short-term wind speed forecasting with Bayesian structural break model. Renewable Energy. 2013; 50: 637-647
    • Liu, H., Tian, H.-Q., Li, Y.-F.. Comparison of new hybrid FEEMD-MLP, FEEMD-ANFIS, Wavelet Packet-MLP and Wavelet Packet-ANFIS for wind speed predictions. Energy Conversion and Management. 2015; 89: 1-11
    • Gupta, R. A., Kumar, R., Bansal, A. K.. BBO-based small autonomous hybrid power system optimization incorporating wind speed and solar radiation forecasting. Renewable and Sustainable Energy Reviews. 2015; 41: 1366-1375
    • Wang, J.-Z., Wang, J.-J., Zhang, Z.-G., Guo, S.-P.. Forecasting stock indices with back propagation neural network. Expert Systems with Applications. 2011; 38 (11): 14346-14355
    • Liu, H., Tian, H.-Q., Chen, C., Li, Y.-F.. A hybrid statistical method to predict wind speed and wind power. Renewable Energy. 2010; 35 (8): 1857-1861
    • Hu, J., Wang, J., Zeng, G.. A hybrid forecasting approach applied to wind speed time series. Renewable Energy. 2013; 60: 185-194
    • Wang, J.-J., Wang, J.-Z., Zhang, Z.-G., Guo, S.-P.. Stock index forecasting based on a hybrid model. Omega. 2012; 40 (6): 758-766
    • Wang, J., Zhu, W., Zhang, W., Sun, D.. A trend fixed on firstly and seasonal adjustment model combined with the ε-SVR for short-term forecasting of electricity demand. Energy Policy. 2009; 37 (11): 4901-4909
    • Salcedo-Sanz, S., Pastor-Sánchez, A., Del Ser, J., Prieto, L., Geem, Z. W.. A coral reefs optimization algorithm with harmony search operatorsfor accurate wind speed prediction. Renewable Energy. 2015; 75: 93-101
    • Wang, J., Zhu, S., Zhang, W., Lu, H.. Combined modeling for electric load forecasting with adaptive particle swarm optimization. Energy. 2010; 35 (4): 1671-1678
    • Moreno, M. Á., Bueno, M., Usaola, J.. Evaluating risk-constrained bidding strategies in adjustment spot markets for wind power producers. International Journal of Electrical Power and Energy Systems. 2012; 43 (1): 703-711
    • Mondal, S., Bhattacharya, A., Nee Dey, S. H.. Multi-objective economic emission load dispatch solution using gravitational search algorithm and considering wind power penetration. International Journal of Electrical Power and Energy Systems. 2013; 44 (1): 282-292
    • Chau, K. W.. Particle swarm optimization training algorithm for ANNs in stage prediction of Shing Mun River. Journal of Hydrology. 2006; 329 (3-4): 363-367
    • Cheng, C. T., Ou, C. P., Chau, K. W.. Combining a fuzzy optimal model with a genetic algorithm to solve multi-objective rainfall-runoff model calibration. Journal of Hydrology. 2002; 268 (1–4): 72-86
    • Flores, P., Tapia, A., Tapia, G.. Application of a control algorithm for wind speed prediction and active power generation. Renewable Energy. 2005; 30 (4): 523-536
    • Guo, Z. X., Wong, W. K., Li, M.. Sparsely connected neural network-based time series forecasting. Information Sciences. 2012; 193: 54-71
    • Hamzaçebi, C.. Improving artificial neural networks' performance in seasonal time series forecasting. Information Sciences. 2008; 178 (23): 4550-4559
    • Kalogirou, S. A.. Artificial neural networks in renewable energy systems applications: a review. Renewable and Sustainable Energy Reviews. 2000; 5 (4): 373-401
    • Lin, J.-Y., Cheng, C.-T., Chau, K.-W.. Using support vector machines for long-term discharge prediction. Hydrological Sciences Journal. 2006; 51 (4): 599-612
    • Lin, K.-P., Pai, P.-F., Lu, Y.-M., Chang, P.-T.. Revenue forecasting using a least-squares support vector regression model in a fuzzy environment. Information Sciences. 2013; 220: 196-209
    • Carolin Mabel, M., Fernandez, E.. Analysis of wind power generation and prediction using ANN: a case study. Renewable Energy. 2008; 33 (5): 986-992
    • Muttil, N., Chau, K.-W.. Neural network and genetic programming for modelling coastal algal blooms. International Journal of Environment and Pollution. 2006; 28 (3-4): 223-238
    • Sfetsos, A.. A comparison of various forecasting techniques applied to mean hourly wind speed time series. Renewable Energy. 2000; 21 (1): 23-35
    • Wu, Q., Law, R., Wu, E., Lin, J. X.. A hybrid-forecasting model reducing Gaussian noise based on the Gaussian support vector regression machine and chaotic particle swarm optimization. Information Sciences. 2013; 238: 96-110
    • Xie, J.-X., Cheng, C.-T., Chau, K.-W., Pei, Y.-Z.. A hybrid adaptive time-delay neural network model for multi-step-ahead prediction of sunspot activity. International Journal of Environment and Pollution. 2006; 28 (3-4): 364-381
    • Zhou, J. Y., Shi, J., Li, G.. Fine tuning support vector machines for short-term wind speed forecasting. Energy Conversion and Management. 2011; 52 (4): 1990-1998
    • Wang, L.-J., Dong, L., Liao, X.-Z., Gao, Y.. Short-term power prediction of a wind farm based on wavelet analysis. Proceedings of the Chinese Society of Electrical Engineering. 2009; 29 (28): 30-33
    • Chen, K., Yu, J.. Short-term wind speed prediction using an unscented Kalman filter based state-space support vector regression approach. Applied Energy. 2014; 113: 690-705
    • Huang, Y.-S., Deng, J.-J., Yuan, Z.-Z.. SVM short-term load forecasting based on ARMA error calibration and the adaptive particle swarm optimization. Power System Protection and Control. 2011; 39 (14): 26-32
    • Bashir, Z. A., El-Hawary, M. E.. Applying wavelets to short-term load forecasting using PSO-based neural networks. IEEE Transactions on Power Systems. 2009; 24 (1): 20-27
    • Xian, D., Bingji, X.. Telecommunication traffic forecasting based on BP neural network trained by PSO. Journal of Central South University. 2011; 42 (1): 24-25
    • Dong, C., Wang, G., Chen, Z., Yu, Z.. A method of self-adaptive inertia weight for PSO. ; 1: 1195-1198
    • Xiu, C., Wang, T., Tian, M., Li, Y., Cheng, Y.. Short-term prediction method of wind speed series basedon fractal interpolation. Chaos, Solitons & Fractals. 2014; 68: 89-97
    • Sheela, K. G., Deepa, S. N.. Neural network based hybrid computing model for wind speed prediction. Neurocomputing. 2013; 122: 425-429
    • Xu, M., Qiao, Y., Lu, Z.. A comprehensive error evaluation method for short-term wind power prediction. Automation of Electric Power Systems. 2011; 35 (12): 20-26
    • Rumelhart, D. E., Hinton, G. E., Williams, R. J.. Learning representations by back-propagating errors. Nature. 1986; 323 (6088): 533-536
    • Elman, J. L.. Finding structure in time. Cognitive Science. 1990; 14 (2): 179-211
    • He, H.-T., Tian, X.. An improved Elman network and its application in flatness prediction modeling. ; 9: 552
    • Marra, S., Morabito, F. C.. A new technique for solar activity forecasting using recurrent Elman networks. World Academy of Science, Engineering and Technology. 2005; 7 (8): 68-73
    • Li, X., Chen, Z., Yuan, Z.. Nonlinear stable adaptive control based upon Elman networks. Applied Mathematics—A Journal of Chinese Universities. 2000; 15 (3): 332-340
    • Nan, X., Li, Q., Qiu, D., Zhao, Y., Guo, X.. Short-term wind speed syntheses correcting forecasting model and its application. International Journal of Electrical Power and Energy Systems. 2013; 49 (1): 264-268
    • Wang, J., Zhu, S., Zhang, W., Lu, H.. Combined modeling for electric load forecasting with adaptive particle swarm optimization. Energy. 2010; 35 (4): 1671-1678
    • Obukhov, A. M.. Statistically homogeneous fields on a sphere. Uspekhi Matematicheskikh Nauk. 1947; 2: 196-198
    • Kutzbach, J. E.. Empirical eigenvectors of sea-level pressure, surface temperature and precipitation complexes over North America. Journal of Applied Meteorology. 1967; 6 (5): 791-802
    • Pearson, K.. On lines and plans of closest fit to system of points in space philosophical. Magazine. 1902; 6: 559-572
    • Lorenz, E. N.. Empirical orthogonal functions and statistical weather prediction. Statistical Forecasting Project. 1956 (1)
    • Davis, R. E.. Predictability of sea surface temperature and sea level pressure anomalies over the North Pacific Ocean. Journal of Physical Oceanography. 1976; 6 (3): 249-266
    • Barnett, T. P., Hasselmann, K.. Techniques of linear prediction, with application to oceanic and atmospheric fields in the tropical Pacific. Reviews of Geophysics & Space Physics. 1979; 17 (5): 949-968
    • Wang, B., Yue, H.. EOF model analysis of place names landscape in Guangdong province on GIS. Scientia Geographica Sinica. 2007; 27 (2): 282-288
    • Zhao, Z., Wang, J., Zhao, J., Su, Z.. Using a Grey model optimized by Differential Evolution algorithm to forecast the per capita annual net income of rural households in China. Omega. 2012; 40 (5): 525-532
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