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First and second order semi-Markov chains for wind speed modeling

Citations

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Cited by:

  1. D’Amico, Guglielmo & Petroni, Filippo & Prattico, Flavio, 2015. "Economic performance indicators of wind energy based on wind speed stochastic modeling," Applied Energy, Elsevier, vol. 154(C), pages 290-297.
  2. Muhammad Yasir & Sitara Afzal & Khalid Latif & Ghulam Mujtaba Chaudhary & Nazish Yameen Malik & Farhan Shahzad & Oh-young Song, 2020. "An Efficient Deep Learning Based Model to Predict Interest Rate Using Twitter Sentiment," Sustainability, MDPI, vol. 12(4), pages 1-16, February.
  3. Guglielmo D'Amico & Filippo Petroni, 2012. "Weighted-indexed semi-Markov models for modeling financial returns," Papers 1205.2551, arXiv.org, revised Jun 2012.
  4. Maegebier, Alexander, 2013. "Valuation and risk assessment of disability insurance using a discrete time trivariate Markov renewal reward process," Insurance: Mathematics and Economics, Elsevier, vol. 53(3), pages 802-811.
  5. Guglielmo D’Amico & Salvatore Vergine, 2026. "Analysis of Semi-Markov Reward Processes Motivated by Ramp Rate Limitation in Wind Farms," Methodology and Computing in Applied Probability, Springer, vol. 28(1), pages 1-43, March.
  6. Chia-Hung Wang & Qigen Zhao & Rong Tian, 2023. "Short-Term Wind Power Prediction Based on a Hybrid Markov-Based PSO-BP Neural Network," Energies, MDPI, vol. 16(11), pages 1-24, May.
  7. Wang, Zhongliang & Zhu, Hongyu & Zhang, Dongdong & Goh, Hui Hwang & Dong, Yunxuan & Wu, Thomas, 2023. "Modelling of wind and photovoltaic power output considering dynamic spatio-temporal correlation," Applied Energy, Elsevier, vol. 352(C).
  8. Amanda S. Hering & Karen Kazor & William Kleiber, 2015. "A Markov-Switching Vector Autoregressive Stochastic Wind Generator for Multiple Spatial and Temporal Scales," Resources, MDPI, vol. 4(1), pages 1-23, February.
  9. Tang, Jie & Brouste, Alexandre & Tsui, Kwok Leung, 2015. "Some improvements of wind speed Markov chain modeling," Renewable Energy, Elsevier, vol. 81(C), pages 52-56.
  10. G. D'Amico & F. Petroni & F. Prattico, 2013. "Semi-Markov Models in High Frequency Finance: A Review," Papers 1312.3894, arXiv.org.
  11. He Yi & Lirong Cui & Narayanaswamy Balakrishnan, 2022. "On the Derivative Counting Processes of First- and Second-order Aggregated Semi-Markov Systems," Methodology and Computing in Applied Probability, Springer, vol. 24(3), pages 1849-1875, September.
  12. Hendradewa, Andrie Pasca & Yin, Shen, 2025. "Comparative analysis of offshore wind turbine blade maintenance: RL-based and classical strategies for sustainable approach," Reliability Engineering and System Safety, Elsevier, vol. 253(C).
  13. Dmitrii Silvestrov & Raimondo Manca, 2017. "Reward Algorithms for Semi-Markov Processes," Methodology and Computing in Applied Probability, Springer, vol. 19(4), pages 1191-1209, December.
  14. Mavromatidis, Georgios & Orehounig, Kristina & Carmeliet, Jan, 2018. "A review of uncertainty characterisation approaches for the optimal design of distributed energy systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 258-277.
  15. Li, Yanting & Wu, Zhenyu, 2020. "A condition monitoring approach of multi-turbine based on VAR model at farm level," Renewable Energy, Elsevier, vol. 166(C), pages 66-80.
  16. Ma, Jinrui & Fouladirad, Mitra & Grall, Antoine, 2018. "Flexible wind speed generation model: Markov chain with an embedded diffusion process," Energy, Elsevier, vol. 164(C), pages 316-328.
  17. Lahmiri, Salim, 2016. "Interest rate next-day variation prediction based on hybrid feedforward neural network, particle swarm optimization, and multiresolution techniques," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 444(C), pages 388-396.
  18. Guglielmo D'Amico & Filippo Petroni, 2013. "Multivariate high-frequency financial data via semi-Markov processes," Papers 1305.0436, arXiv.org.
  19. Hui Hwang Goh & Gumeng Peng & Dongdong Zhang & Wei Dai & Tonni Agustiono Kurniawan & Kai Chen Goh & Chin Leei Cham, 2022. "A New Wind Speed Scenario Generation Method Based on Principal Component and R-Vine Copula Theories," Energies, MDPI, vol. 15(7), pages 1-21, April.
  20. Yi, He & Cui, Lirong, 2017. "Distribution and availability for aggregated second-order semi-Markov ternary system with working time omission," Reliability Engineering and System Safety, Elsevier, vol. 166(C), pages 50-60.
  21. Fang, Chen & Cui, Lirong, 2021. "Reliability evaluation for balanced systems with auto-balancing mechanisms," Reliability Engineering and System Safety, Elsevier, vol. 213(C).
  22. D׳Amico, Guglielmo & Petroni, Filippo & Prattico, Flavio, 2015. "Reliability measures for indexed semi-Markov chains applied to wind energy production," Reliability Engineering and System Safety, Elsevier, vol. 144(C), pages 170-177.
  23. Riccardo De Blasis, 2023. "Weighted-indexed semi-Markov model: calibration and application to financial modeling," Financial Innovation, Springer;Southwestern University of Finance and Economics, vol. 9(1), pages 1-16, December.
  24. D’Amico, Guglielmo & Petroni, Filippo & Prattico, Flavio, 2014. "Wind speed and energy forecasting at different time scales: A nonparametric approach," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 406(C), pages 59-66.
  25. Yi, He & Cui, Lirong & Balakrishnan, Narayanaswamy, 2021. "New reliability indices for first- and second-order discrete-time aggregated semi-Markov systems with an application to TT&C system," Reliability Engineering and System Safety, Elsevier, vol. 215(C).
  26. Jónsdóttir, Guðrún Margrét & Milano, Federico, 2019. "Data-based continuous wind speed models with arbitrary probability distribution and autocorrelation," Renewable Energy, Elsevier, vol. 143(C), pages 368-376.
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