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First and second order Markov chain models for synthetic generation of wind speed time series

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  1. Goić, R. & Krstulović, J. & Jakus, D., 2010. "Simulation of aggregate wind farm short-term production variations," Renewable Energy, Elsevier, vol. 35(11), pages 2602-2609.
  2. 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.
  3. Jinlin Li & Lanhui Zhang & Chansheng He & Chen Zhao, 2018. "A Comparison of Markov Chain Random Field and Ordinary Kriging Methods for Calculating Soil Texture in a Mountainous Watershed, Northwest China," Sustainability, MDPI, vol. 10(8), pages 1-18, August.
  4. Carapellucci, Roberto & Giordano, Lorena, 2013. "A methodology for the synthetic generation of hourly wind speed time series based on some known aggregate input data," Applied Energy, Elsevier, vol. 101(C), pages 541-550.
  5. Haghighat, Fatemeh, 2021. "Predicting the trend of indicators related to Covid-19 using the combined MLP-MC model," Chaos, Solitons & Fractals, Elsevier, vol. 152(C).
  6. Khasanzoda, Nasrullo & Zicmane, Inga & Beryozkina, Svetlana & Safaraliev, Murodbek & Sultonov, Sherkhon & Kirgizov, Alifbek, 2022. "Regression model for predicting the speed of wind flows for energy needs based on fuzzy logic," Renewable Energy, Elsevier, vol. 191(C), pages 723-731.
  7. Chiacchio, Ferdinando & D’Urso, Diego & Famoso, Fabio & Brusca, Sebastian & Aizpurua, Jose Ignacio & Catterson, Victoria M., 2018. "On the use of dynamic reliability for an accurate modelling of renewable power plants," Energy, Elsevier, vol. 151(C), pages 605-621.
  8. 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.
  9. 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.
  10. Lei, Yang & Wang, Dan & Jia, Hongjie & Li, Jiaxi & Chen, Jingcheng & Li, Jingru & Yang, Zhihong, 2021. "Multi-stage stochastic planning of regional integrated energy system based on scenario tree path optimization under long-term multiple uncertainties," Applied Energy, Elsevier, vol. 300(C).
  11. Carapellucci, Roberto & Giordano, Lorena, 2013. "The effect of diurnal profile and seasonal wind regime on sizing grid-connected and off-grid wind power plants," Applied Energy, Elsevier, vol. 107(C), pages 364-376.
  12. Duan, Jikai & Zuo, Hongchao & Bai, Yulong & Duan, Jizheng & Chang, Mingheng & Chen, Bolong, 2021. "Short-term wind speed forecasting using recurrent neural networks with error correction," Energy, Elsevier, vol. 217(C).
  13. 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.
  14. Jujie Wang & Yanfeng Wang & Yaning Li, 2018. "A Novel Hybrid Strategy Using Three-Phase Feature Extraction and a Weighted Regularized Extreme Learning Machine for Multi-Step Ahead Wind Speed Prediction," Energies, MDPI, vol. 11(2), pages 1-33, February.
  15. Agrawal, Alok & Sandhu, Kanwarjit Singh, 2016. "Most influential parametrical and data needs for realistic wind speed prediction," Renewable Energy, Elsevier, vol. 94(C), pages 452-465.
  16. Lujano-Rojas, Juan M. & Dufo-López, Rodolfo & Bernal-Agustín, José L., 2013. "Probabilistic modelling and analysis of stand-alone hybrid power systems," Energy, Elsevier, vol. 63(C), pages 19-27.
  17. Pinciroli, Luca & Baraldi, Piero & Ballabio, Guido & Compare, Michele & Zio, Enrico, 2022. "Optimization of the Operation and Maintenance of renewable energy systems by Deep Reinforcement Learning," Renewable Energy, Elsevier, vol. 183(C), pages 752-763.
  18. 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.
  19. 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.
  20. Meschede, Henning & Dunkelberg, Heiko & Stöhr, Fabian & Peesel, Ron-Hendrik & Hesselbach, Jens, 2017. "Assessment of probabilistic distributed factors influencing renewable energy supply for hotels using Monte-Carlo methods," Energy, Elsevier, vol. 128(C), pages 86-100.
  21. Damásio, Bruno & Nicolau, João, 2024. "Time inhomogeneous multivariate Markov chains: Detecting and testing multiple structural breaks occurring at unknown dates," Chaos, Solitons & Fractals, Elsevier, vol. 180(C).
  22. Hong, Ying-Yi & Chang, Huei-Lin & Chiu, Ching-Sheng, 2010. "Hour-ahead wind power and speed forecasting using simultaneous perturbation stochastic approximation (SPSA) algorithm and neural network with fuzzy inputs," Energy, Elsevier, vol. 35(9), pages 3870-3876.
  23. Nikolaos Stavropoulos & Alexandra Papadopoulou & Pavlos Kolias, 2021. "Evaluating the Efficiency of Off-Ball Screens in Elite Basketball Teams via Second-Order Markov Modelling," Mathematics, MDPI, vol. 9(16), pages 1-13, August.
  24. Luca Pinciroli & Piero Baraldi & Guido Ballabio & Michele Compare & Enrico Zio, 2021. "Deep Reinforcement Learning Based on Proximal Policy Optimization for the Maintenance of a Wind Farm with Multiple Crews," Energies, MDPI, vol. 14(20), pages 1-17, October.
  25. Yousif Alyousifi & Kamarulzaman Ibrahim & Mahmod Othamn & Wan Zawiah Wan Zin & Nicolas Vergne & Abdullah Al-Yaari, 2022. "Bayesian Information Criterion for Fitting the Optimum Order of Markov Chain Models: Methodology and Application to Air Pollution Data," Mathematics, MDPI, vol. 10(13), pages 1-16, June.
  26. 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.
  27. Abdel-Aal, R.E. & Elhadidy, M.A. & Shaahid, S.M., 2009. "Modeling and forecasting the mean hourly wind speed time series using GMDH-based abductive networks," Renewable Energy, Elsevier, vol. 34(7), pages 1686-1699.
  28. Yirgalem Chebud & Assefa Melesse, 2011. "Operational Prediction of Groundwater Fluctuation in South Florida using Sequence Based Markovian Stochastic Model," Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), Springer;European Water Resources Association (EWRA), vol. 25(9), pages 2279-2294, July.
  29. Duan, Jikai & Chang, Mingheng & Chen, Xiangyue & Wang, Wenpeng & Zuo, Hongchao & Bai, Yulong & Chen, Bolong, 2022. "A combined short-term wind speed forecasting model based on CNN–RNN and linear regression optimization considering error," Renewable Energy, Elsevier, vol. 200(C), pages 788-808.
  30. Liu, Heping & Shi, Jing & Erdem, Ergin, 2010. "Prediction of wind speed time series using modified Taylor Kriging method," Energy, Elsevier, vol. 35(12), pages 4870-4879.
  31. D’Amico, Guglielmo & Petroni, Filippo & Prattico, Flavio, 2013. "First and second order semi-Markov chains for wind speed modeling," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 392(5), pages 1194-1201.
  32. Karami, M. & Shayanfar, H.A. & Aghaei, J. & Ahmadi, A., 2013. "Scenario-based security-constrained hydrothermal coordination with volatile wind power generation," Renewable and Sustainable Energy Reviews, Elsevier, vol. 28(C), pages 726-737.
  33. Zhang, Jin-hua & Liu, Yong-qian & Tian, De & Yan, Jie, 2015. "Optimal power dispatch in wind farm based on reduced blade damage and generator losses," Renewable and Sustainable Energy Reviews, Elsevier, vol. 44(C), pages 64-77.
  34. Bruno Damásio & João Nicolau, 2020. "Time Inhomogeneous Multivariate Markov Chains: Detecting and Testing Multiple Structural Breaks Occurring at Unknown," Working Papers REM 2020/0136, ISEG - Lisbon School of Economics and Management, REM, Universidade de Lisboa.
  35. Coville, Aidan & Siddiqui, Afzal & Vogstad, Klaus-Ole, 2011. "The effect of missing data on wind resource estimation," Energy, Elsevier, vol. 36(7), pages 4505-4517.
  36. Nuño Martinez, Edgar & Cutululis, Nicolaos & Sørensen, Poul, 2018. "High dimensional dependence in power systems: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 94(C), pages 197-213.
  37. Wang, Ying & Wang, Jianzhou & Li, Zhiwu & Yang, Hufang & Li, Hongmin, 2021. "Design of a combined system based on two-stage data preprocessing and multi-objective optimization for wind speed prediction," Energy, Elsevier, vol. 231(C).
  38. Zhihao Shang & Quan Wen & Yanhua Chen & Bing Zhou & Mingliang Xu, 2022. "Wind Speed Forecasting Using Attention-Based Causal Convolutional Network and Wind Energy Conversion," Energies, MDPI, vol. 15(8), pages 1-23, April.
  39. Rundong Ge & Wenying Liu & Huiyong Li & Jianzong Zhuo & Weizhou Wang, 2015. "Research on the Multi-Period Small-Signal Stability Probability of a Power System with Wind Farms Based on the Markov Chain," Sustainability, MDPI, vol. 7(4), pages 1-18, April.
  40. Evans, S.P. & Clausen, P.D., 2015. "Modelling of turbulent wind flow using the embedded Markov chain method," Renewable Energy, Elsevier, vol. 81(C), pages 671-678.
  41. Aasim, & Singh, S.N. & Mohapatra, Abheejeet, 2019. "Repeated wavelet transform based ARIMA model for very short-term wind speed forecasting," Renewable Energy, Elsevier, vol. 136(C), pages 758-768.
  42. Masseran, Nurulkamal, 2016. "Modeling the fluctuations of wind speed data by considering their mean and volatility effects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 54(C), pages 777-784.
  43. Zheng, Yi & You, Shi & Huang, Chunjun & Jin, Xin, 2023. "Model-based economic analysis of off-grid wind/hydrogen systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 187(C).
  44. Kerolly Kedma Felix do Nascimento & Fábio Sandro dos Santos & Jader Silva Jale & Silvio Fernando Alves Xavier Júnior & Tiago A. E. Ferreira, 2023. "Extracting Rules via Markov Chains for Cryptocurrencies Returns Forecasting," Computational Economics, Springer;Society for Computational Economics, vol. 61(3), pages 1095-1114, March.
  45. 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.
  46. Feijóo, Andrés & Villanueva, Daniel, 2016. "Assessing wind speed simulation methods," Renewable and Sustainable Energy Reviews, Elsevier, vol. 56(C), pages 473-483.
  47. Scholz, Teresa & Lopes, Vitor V. & Estanqueiro, Ana, 2014. "A cyclic time-dependent Markov process to model daily patterns in wind turbine power production," Energy, Elsevier, vol. 67(C), pages 557-568.
  48. Moghimi Ghadikolaei, Hadi & Ahmadi, Abdollah & Aghaei, Jamshid & Najafi, Meysam, 2012. "Risk constrained self-scheduling of hydro/wind units for short term electricity markets considering intermittency and uncertainty," Renewable and Sustainable Energy Reviews, Elsevier, vol. 16(7), pages 4734-4743.
  49. 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.
  50. Hong, Ying-Yi & Chang, Wen-Chun & Chang, Yung-Ruei & Lee, Yih-Der & Ouyang, Der-Chuan, 2017. "Optimal sizing of renewable energy generations in a community microgrid using Markov model," Energy, Elsevier, vol. 135(C), pages 68-74.
  51. Carapellucci, Roberto & Giordano, Lorena, 2013. "A new approach for synthetically generating wind speeds: A comparison with the Markov chains method," Energy, Elsevier, vol. 49(C), pages 298-305.
  52. Asif Iqbal & Syed Ahmad Hassan, 2018. "ENSO and IOD analysis on the occurrence of floods in Pakistan," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 91(3), pages 879-890, April.
  53. Mohammadi Rad, Amin & Barforoushi, Taghi, 2020. "Optimal scheduling of resources and appliances in smart homes under uncertainties considering participation in spot and contractual markets," Energy, Elsevier, vol. 192(C).
  54. Carta, J.A. & Ramírez, P. & Velázquez, S., 2009. "A review of wind speed probability distributions used in wind energy analysis: Case studies in the Canary Islands," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(5), pages 933-955, June.
  55. Suomalainen, K. & Silva, C.A. & Ferrão, P. & Connors, S., 2012. "Synthetic wind speed scenarios including diurnal effects: Implications for wind power dimensioning," Energy, Elsevier, vol. 37(1), pages 41-50.
  56. Vauhkonen, Jari & Packalen, Tuula, 2018. "Uncertainties related to climate change and forest management with implications on climate regulation in Finland," Ecosystem Services, Elsevier, vol. 33(PB), pages 213-224.
  57. Kuznetsova, Elizaveta & Li, Yan-Fu & Ruiz, Carlos & Zio, Enrico & Ault, Graham & Bell, Keith, 2013. "Reinforcement learning for microgrid energy management," Energy, Elsevier, vol. 59(C), pages 133-146.
  58. Kerkhove, L.-P. & Vanhoucke, M., 2017. "Optimised scheduling for weather sensitive offshore construction projects," Omega, Elsevier, vol. 66(PA), pages 58-78.
  59. 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.
  60. Li, Gong & Shi, Jing & Zhou, Junyi, 2011. "Bayesian adaptive combination of short-term wind speed forecasts from neural network models," Renewable Energy, Elsevier, vol. 36(1), pages 352-359.
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