Total views : 163

Short Term Wind Speed Forecasting using Hybrid ELM Approach

Affiliations

  • EEE Department, UCEK, JNTUK, Kakinada - 533003, Andhra Pradesh, India

Abstract


Objectives: To enrich the accuracy of the wind speed forecasting to calculate wind power generation connecting to grid with the help of machine learning algorithms. Methods/Statistical Analysis: A novel hybrid method, Persistence - Extreme Learning Machine (P-ELM) algorithm is proposed. It uses the features of both Persistent and Extreme Learning Machine algorithms. The historical data of meteorological parameters viz. pressure, temperature and past wind speeds are considered as input parameters and wind speed for the next instant as the output, measured at one hour interval for a period of a month are used for the study. Findings: The forecasting is carried out for three areas Guntur, Vijayawada and Ongole of Andhra Pradesh state, India for winter, summer and rainy seasons. The performance of the P-ELM is evaluated in terms of Mean Absolute Error (MAE) and Root Mean Square Error (RMSE). From the results obtained it can be concluded that the P-ELM algorithm leaves behind the fundamental Persistent and Extreme Learning Machine algorithm not only in terms of error metrics but also in simulation time. The entire simulation is carried out with the help of MATLAB 2013a software. Application/Improvements: This technique is very much helpful in real time forecasting of wind speed to avoid the high level of uncertainty involved in wind generation, there by increases the power system security and stability.

Keywords

Extreme Learning Machine (ELM), Forecasting, Machine Learning Algorithms, Persistent Extreme Learning Machine (P-ELM), Wind Speed

Full Text:

 |  (PDF views: 138)

References


  • Wan C, Xu Z, Pinson P, Dong ZY, Wong KP. Probabilistic forecasting of wind power generation using extreme learning machine. IEEE Transaction on Power Systems. 2014; 29(3):1033–44. Crossref
  • Wan C, Xu Z, Pinson P, Dong ZY, Wong KP. Optimal prediction intervals of wind power generation. IEEE Transaction on Power Systems. 2014; 29(3):1166–74. Crossref
  • Haque A, Hashem Nehri M, Mandal P. A Hybrid Intelligent Model for Deterministic and Quantile Regression Approach for Probabilistic Wind Power Forecasting. IEEE Transactions on Power Systems. 2014; 29(4):1663–72. Crossref
  • Bruninx K, Delarue E. A statistical description of the error on wind power forecasts for probabilistic reserve sizing. IEEE Transactions on Sustainable Energy. 2014; 5(3):995–1002. Crossref
  • Tastu J, Pinson P, Trombe P, Madsen H. Probabilistic Forecasts of Wind Power Generation Accounting for Geographically Dispersed Information. IEEE Transactions on Smart Grid. 2014; 5(1):480–89.Crossref
  • Erdem E, Shi J, Peng Y. Short-Term Forecasting of Wind Speed and Power - A Clustering Approach. Conference Proceedings on Industrial and Systems Engineering Research, Canada. 2014. p. 1–8.
  • Mbamalu G, Harding A. A Deterministic Bases Piecewise Wind Power Forecasting Models. International Journal of Renewable Energy Research. 2014; 4(1):137–43.
  • Abdullah AA, Saleh AE, Moustafa MS, Abo-al-Ez KM. A proposed Framework for a Forecasting System of Wind Energy Power Generation. International Journal of Advanced Research in Computer Engineering and Technology (IJARCET). 2014; 3(4):1051–57.
  • Ghadi MJ, Gilani SH, Afrakhte H, Baghramian A. ShortTerm and Very Short-Term Wind Power Forecasting Using a Hybrid ICA-NN Method. International Journal of Computing and Digital Systems. 2014; 3(1):61–8.
  • Li Y, Yang R. A hybrid algorithm combining auto-encoder network with sparse Bayesian regression optimized by artificial bee colony for short-term wind power forecasting. PRZEGLĄD ELEKTROTECHNICZNY. 2013; 89(2):223–8.
  • Yona A, Senjyu T, Toshihisa F, Kim C. Very ShortTerm Generating Power Forecasting for Wind Power Generators Based on Time Series Analysis. Smart Grid and Renewable Energy. 2013; 4:181–86.
  • Crossref
  • Chang W. Short-Term Wind Power Forecasting Using the Enhanced Particle Swarm Optimization
  • Based Hybrid Method. Energies. 2013; 6:4879–96. Crossref
  • Sideratos G, Hatziargyriou ND. Probabilistic wind power forecasting using radial basis function neural networks. IEEE Transactions on Power Systems. 2012; 27(4):1788–96. Crossref
  • Zheng Z, Chen Y, Zhou X, Huo M, Zhao B, Guo M.Short-Term Wind Power Forecasting Using Empirical Mode Decomposition and RBFNN. International Journal of Smart Grid and Clean Energy. 2013; 2(2):192–99.Crossref
  • Catalao JPS, Pousinho HMI, Mendes VMF. New hybrid intelligent approach to forecast wind power and electricity prices in the short-term. 17th Power Systems Computation Conference. Stockholm, Sweden. 2011. p. 1–7.
  • Botterud A, Wang J. Wind Power Forecasting in U.S. Electricity Markets. Electricity Journal. 2010; 23(3):71–82.Crossref
  • Aggarwal SK, Gupta M. Wind Power Forecasting: A Review of Statistical Models. International Journal of Energy Science (IJES). 2013; 3(1):1–10.
  • Makarov YV, Loutan C, Ma J, Mello P. Operational Impacts of Wind Generation on California Power Systems.IEEE Transactions on Power Systems. 2009; 24(2):1039–50. Crossref
  • Kiviluoma J, Meibom P, Tuohy A, Troy N, Milligan M, Lange B, Gibescu M, O’Malley M. Short-Term Energy Balancing With Increasing Levels of Wind Energy. IEEE Transactions on Sustainable Energy. 2012; 3(4):769–76. Crossref
  • Gopi P, Vaidyanathan SG, Habiba HU, Kalyani S. Comparative analysis of wind power forecasting using artificial neural network (ANN). International Journal of Engineering and Computer Science. 2012; 1(3):178–86.
  • Chang W. A Literature Review of Wind Forecasting Methods.Journal of Power and Energy Engineering. 2014; 2:161–68.Crossref
  • Choudhary AK, Upadhyay KG, Tripathi MM. Estimation of wind power using different soft computing methods. International Journal of Electrical Systems (IJES). 2011; 1(1):1–7.
  • Barbounis TG, Theocharis JB, Alexiadis MC, Dokopoulos PS. Long term wind speed and power forecasting using local recurrent neural network models. IEEE Transactions on Energy Conversion. 2006; 21(1):273–84.Crossref
  • Mao J, Zhang X, Li J. Wind Power Forecasting Based on the BP Neural Network. Proceedings of the 2nd International Conference on Systems Engineering and Modeling, Beijing, China. 2013. p. 13–7.
  • Crossref
  • Rajper S, Aijaz A, Kalhoro. Optimal Wind Turbine Micrositing: A Case of Multi-Directional and Uniform Wind Speed.Indian Journal of Science and Technology. 2016 Jul; 9(26):1–3.Crossref
  • Izadbakhsh M, Rezvani A, Gandomkar M, Mirsaeidi S.Dynamic Analysis of PMSG Wind Turbine under Variable Wind Speeds and Load Conditions in the Grid Connected Mode. Indian Journal of Science and Technology. 2015 Jul; 8(14):1–7. Crossref
  • Shamshirband S, Mohammadi K, Tong CW, Petkovic D, Porcu E, Mostafaeipour A, Sudheer C, Sedaghat A. Application of extreme learning machine for estimation of wind speed distribution.Climate Dynamics. Springer. 2016; 46(5):1893–907. Crossref
  • Nagalingam KK, Ramasamy S, Mamun A. Extreme Learning Machine for Prediction of Wind Force and Moment Coefficients on Marine Vessels. Indian Journal of Science and Technology. 2016 Aug; 9(29):1–8.
  • Crossref
  • SairaBanu J, Babu R, Pandey R. Parallel Implementation of Singular Value Decomposition (SVD) in Image Compression using Open Mp and Sparse Matrix Representation. Indian Journal of Science and Technology. 2015 Jul; 8(13):1–10.
  • Jadav RA, Patel SS. Application of Singular Value Decomposition in Image Processing. Indian Journal of Science and Technology. 2010 Feb; 3(2):1–10.
  • Prasad KR, Rao TCM, Kannan V. A Novel and Hybrid Secure Digital Image Watermarking Framework through SCLWTSVD. Indian Journal of Science and Technology. 2016 Jun; 9(23):1–10.
  • Campbell PRJ, Adamson K. A Novel Approach to Wind Forecasting In the United Kingdom and Ireland. International Journal of Simulation. 2013; 6(12):1–10.

Refbacks

  • There are currently no refbacks.


Creative Commons License
This work is licensed under a Creative Commons Attribution 3.0 License.