IDEAS home Printed from https://ideas.repec.org/r/eee/energy/v36y2011i5p2671-2685.html
   My bibliography  Save this item

A new probabilistic method to estimate the long-term wind speed characteristics at a potential wind energy conversion site

Citations

Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
as


Cited by:

  1. Muhammad Sufyan & Nasrudin Abd Rahim & ChiaKwang Tan & Munir Azam Muhammad & Siti Rohani Sheikh Raihan, 2019. "Optimal sizing and energy scheduling of isolated microgrid considering the battery lifetime degradation," PLOS ONE, Public Library of Science, vol. 14(2), pages 1-28, February.
  2. Oh, Ki-Yong & Kim, Ji-Young & Lee, Jae-Kyung & Ryu, Moo-Sung & Lee, Jun-Shin, 2012. "An assessment of wind energy potential at the demonstration offshore wind farm in Korea," Energy, Elsevier, vol. 46(1), pages 555-563.
  3. Wang, Jianzhou & Qin, Shanshan & Zhou, Qingping & Jiang, Haiyan, 2015. "Medium-term wind speeds forecasting utilizing hybrid models for three different sites in Xinjiang, China," Renewable Energy, Elsevier, vol. 76(C), pages 91-101.
  4. Carta, José A. & Velázquez, Sergio & Cabrera, Pedro, 2013. "A review of measure-correlate-predict (MCP) methods used to estimate long-term wind characteristics at a target site," Renewable and Sustainable Energy Reviews, Elsevier, vol. 27(C), pages 362-400.
  5. Kim, Ji-Young & Oh, Ki-Yong & Kim, Min-Suek & Kim, Kwang-Yul, 2019. "Evaluation and characterization of offshore wind resources with long-term met mast data corrected by wind lidar," Renewable Energy, Elsevier, vol. 144(C), pages 41-55.
  6. Firouzmakan, Pouya & Hooshmand, Rahmat-Allah & Bornapour, Mosayeb & Khodabakhshian, Amin, 2019. "A comprehensive stochastic energy management system of micro-CHP units, renewable energy sources and storage systems in microgrids considering demand response programs," Renewable and Sustainable Energy Reviews, Elsevier, vol. 108(C), pages 355-368.
  7. Lena Kitzing & Christoph Weber, "undated". "Support mechanisms for renewables: How risk exposure influences investment incentives," EWL Working Papers 1403, University of Duisburg-Essen, Chair for Management Science and Energy Economics, revised Aug 2014.
  8. Schallenberg-Rodriguez, Julieta, 2013. "A methodological review to estimate techno-economical wind energy production," Renewable and Sustainable Energy Reviews, Elsevier, vol. 21(C), pages 272-287.
  9. Zhu, Y. & Li, Y.P. & Huang, G.H. & Fu, D.Z., 2013. "Modeling for planning municipal electric power systems associated with air pollution control – A case study of Beijing," Energy, Elsevier, vol. 60(C), pages 168-186.
  10. Xiao Liu & Xu Lai & Jin Zou, 2017. "A New MCP Method of Wind Speed Temporal Interpolation and Extrapolation Considering Wind Speed Mixed Uncertainty," Energies, MDPI, vol. 10(8), pages 1-21, August.
  11. Wang, Chengshan & Liu, Yixin & Li, Xialin & Guo, Li & Qiao, Lei & Lu, Hai, 2016. "Energy management system for stand-alone diesel-wind-biomass microgrid with energy storage system," Energy, Elsevier, vol. 97(C), pages 90-104.
  12. José V. P. Miguel & Eliane A. Fadigas & Ildo L. Sauer, 2019. "The Influence of the Wind Measurement Campaign Duration on a Measure-Correlate-Predict (MCP)-Based Wind Resource Assessment," Energies, MDPI, vol. 12(19), pages 1-15, September.
  13. Tosunoğlu, Fatih, 2018. "Accurate estimation of T year extreme wind speeds by considering different model selection criterions and different parameter estimation methods," Energy, Elsevier, vol. 162(C), pages 813-824.
  14. Soukissian, Takvor H. & Karathanasi, Flora E., 2017. "On the selection of bivariate parametric models for wind data," Applied Energy, Elsevier, vol. 188(C), pages 280-304.
  15. Yu, Jie & Chen, Kuilin & Mori, Junichi & Rashid, Mudassir M., 2013. "A Gaussian mixture copula model based localized Gaussian process regression approach for long-term wind speed prediction," Energy, Elsevier, vol. 61(C), pages 673-686.
  16. Kang, Dongbum & Ko, Kyungnam & Huh, Jongchul, 2015. "Determination of extreme wind values using the Gumbel distribution," Energy, Elsevier, vol. 86(C), pages 51-58.
  17. Han, Qinkai & Hao, Zhuolin & Hu, Tao & Chu, Fulei, 2018. "Non-parametric models for joint probabilistic distributions of wind speed and direction data," Renewable Energy, Elsevier, vol. 126(C), pages 1032-1042.
  18. Carta, José A. & Cabrera, Pedro & Matías, José M. & Castellano, Fernando, 2015. "Comparison of feature selection methods using ANNs in MCP-wind speed methods. A case study," Applied Energy, Elsevier, vol. 158(C), pages 490-507.
  19. Mifsud, Michael D. & Sant, Tonio & Farrugia, Robert N., 2018. "A comparison of Measure-Correlate-Predict Methodologies using LiDAR as a candidate site measurement device for the Mediterranean Island of Malta," Renewable Energy, Elsevier, vol. 127(C), pages 947-959.
  20. Weekes, S.M. & Tomlin, A.S., 2014. "Comparison between the bivariate Weibull probability approach and linear regression for assessment of the long-term wind energy resource using MCP," Renewable Energy, Elsevier, vol. 68(C), pages 529-539.
  21. Oluseyi O. Ajayi & Richard O. Fagbenle & James Katende & Julius M. Ndambuki & David O. Omole & Adekunle A. Badejo, 2014. "Wind Energy Study and Energy Cost of Wind Electricity Generation in Nigeria: Past and Recent Results and a Case Study for South West Nigeria," Energies, MDPI, vol. 7(12), pages 1-27, December.
  22. Jiang, Haiyan & Wang, Jianzhou & Wu, Jie & Geng, Wei, 2017. "Comparison of numerical methods and metaheuristic optimization algorithms for estimating parameters for wind energy potential assessment in low wind regions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 69(C), pages 1199-1217.
  23. Woochul Nam & Ki-Yong Oh, 2020. "Mutually Complementary Measure-Correlate-Predict Method for Enhanced Long-Term Wind-Resource Assessment," Mathematics, MDPI, vol. 8(10), pages 1-20, October.
  24. Dinler, Ali, 2013. "A new low-correlation MCP (measure-correlate-predict) method for wind energy forecasting," Energy, Elsevier, vol. 63(C), pages 152-160.
  25. Azizipanah-Abarghooee, Rasoul & Niknam, Taher & Roosta, Alireza & Malekpour, Ahmad Reza & Zare, Mohsen, 2012. "Probabilistic multiobjective wind-thermal economic emission dispatch based on point estimated method," Energy, Elsevier, vol. 37(1), pages 322-335.
  26. Sharma, Sharmistha & Bhattacharjee, Subhadeep & Bhattacharya, Aniruddha, 2018. "Probabilistic operation cost minimization of Micro-Grid," Energy, Elsevier, vol. 148(C), pages 1116-1139.
  27. Han, Qinkai & Ma, Sai & Wang, Tianyang & Chu, Fulei, 2019. "Kernel density estimation model for wind speed probability distribution with applicability to wind energy assessment in China," Renewable and Sustainable Energy Reviews, Elsevier, vol. 115(C).
  28. Pousinho, H.M.I. & Silva, H. & Mendes, V.M.F. & Collares-Pereira, M. & Pereira Cabrita, C., 2014. "Self-scheduling for energy and spinning reserve of wind/CSP plants by a MILP approach," Energy, Elsevier, vol. 78(C), pages 524-534.
  29. Kitzing, Lena, 2014. "Risk implications of renewable support instruments: Comparative analysis of feed-in tariffs and premiums using a mean–variance approach," Energy, Elsevier, vol. 64(C), pages 495-505.
  30. Niknam, Taher & Golestaneh, Faranak & Malekpour, Ahmadreza, 2012. "Probabilistic energy and operation management of a microgrid containing wind/photovoltaic/fuel cell generation and energy storage devices based on point estimate method and self-adaptive gravitational," Energy, Elsevier, vol. 43(1), pages 427-437.
  31. Incheol Shin, 2020. "Approximation Algorithm-Based Prosumer Scheduling for Microgrids," Energies, MDPI, vol. 13(21), pages 1-16, November.
  32. Troncoso, A. & Salcedo-Sanz, S. & Casanova-Mateo, C. & Riquelme, J.C. & Prieto, L., 2015. "Local models-based regression trees for very short-term wind speed prediction," Renewable Energy, Elsevier, vol. 81(C), pages 589-598.
  33. Velázquez, Sergio & Carta, José A. & Matías, J.M., 2011. "Comparison between ANNs and linear MCP algorithms in the long-term estimation of the cost per kWh produced by a wind turbine at a candidate site: A case study in the Canary Islands," Applied Energy, Elsevier, vol. 88(11), pages 3869-3881.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.