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Short-term wind power forecasting in Portugal by neural networks and wavelet transform

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Cited by:

  1. Flores, Juan J. & Graff, Mario & Rodriguez, Hector, 2012. "Evolutive design of ARMA and ANN models for time series forecasting," Renewable Energy, Elsevier, vol. 44(C), pages 225-230.
  2. Dehua Zheng & Min Shi & Yifeng Wang & Abinet Tesfaye Eseye & Jianhua Zhang, 2017. "Day-Ahead Wind Power Forecasting Using a Two-Stage Hybrid Modeling Approach Based on SCADA and Meteorological Information, and Evaluating the Impact of Input-Data Dependency on Forecasting Accuracy," Energies, MDPI, vol. 10(12), pages 1-23, December.
  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. Couto, António & Estanqueiro, Ana, 2022. "Enhancing wind power forecast accuracy using the weather research and forecasting numerical model-based features and artificial neuronal networks," Renewable Energy, Elsevier, vol. 201(P1), pages 1076-1085.
  5. Gerardo J. Osório & Jorge N. D. L. Gonçalves & Juan M. Lujano-Rojas & João P. S. Catalão, 2016. "Enhanced Forecasting Approach for Electricity Market Prices and Wind Power Data Series in the Short-Term," Energies, MDPI, vol. 9(9), pages 1-19, August.
  6. Tascikaraoglu, Akin & Sanandaji, Borhan M. & Poolla, Kameshwar & Varaiya, Pravin, 2016. "Exploiting sparsity of interconnections in spatio-temporal wind speed forecasting using Wavelet Transform," Applied Energy, Elsevier, vol. 165(C), pages 735-747.
  7. Silva, Ana R. & Pousinho, H.M.I. & Estanqueiro, Ana, 2022. "A multistage stochastic approach for the optimal bidding of variable renewable energy in the day-ahead, intraday and balancing markets," Energy, Elsevier, vol. 258(C).
  8. Bazine, Hasnaa & Mabrouki, Mustapha, 2019. "Chaotic dynamics applied in time prediction of photovoltaic production," Renewable Energy, Elsevier, vol. 136(C), pages 1255-1265.
  9. Hoolohan, Victoria & Tomlin, Alison S. & Cockerill, Timothy, 2018. "Improved near surface wind speed predictions using Gaussian process regression combined with numerical weather predictions and observed meteorological data," Renewable Energy, Elsevier, vol. 126(C), pages 1043-1054.
  10. Wang, Yun & Wang, Jianzhou & Wei, Xiang, 2015. "A hybrid wind speed forecasting model based on phase space reconstruction theory and Markov model: A case study of wind farms in northwest China," Energy, Elsevier, vol. 91(C), pages 556-572.
  11. Osório, G.J. & Matias, J.C.O. & Catalão, J.P.S., 2015. "Short-term wind power forecasting using adaptive neuro-fuzzy inference system combined with evolutionary particle swarm optimization, wavelet transform and mutual information," Renewable Energy, Elsevier, vol. 75(C), pages 301-307.
  12. Sandra Minerva Valdivia-Bautista & José Antonio Domínguez-Navarro & Marco Pérez-Cisneros & Carlos Jesahel Vega-Gómez & Beatriz Castillo-Téllez, 2023. "Artificial Intelligence in Wind Speed Forecasting: A Review," Energies, MDPI, vol. 16(5), pages 1-28, March.
  13. Khazaei, Sahra & Ehsan, Mehdi & Soleymani, Soodabeh & Mohammadnezhad-Shourkaei, Hosein, 2022. "A high-accuracy hybrid method for short-term wind power forecasting," Energy, Elsevier, vol. 238(PC).
  14. Hu, Jianming & Wang, Jianzhou, 2015. "Short-term wind speed prediction using empirical wavelet transform and Gaussian process regression," Energy, Elsevier, vol. 93(P2), pages 1456-1466.
  15. Ioannis K. Bazionis & Markos A. Kousounadis-Knudsen & Theodoros Konstantinou & Pavlos S. Georgilakis, 2021. "A WT-LUBE-PSO-CWC Wind Power Probabilistic Forecasting Model for Prediction Interval Construction and Seasonality Analysis," Energies, MDPI, vol. 14(18), pages 1-23, September.
  16. Zhao, Pan & Wang, Jiangfeng & Xia, Junrong & Dai, Yiping & Sheng, Yingxin & Yue, Jie, 2012. "Performance evaluation and accuracy enhancement of a day-ahead wind power forecasting system in China," Renewable Energy, Elsevier, vol. 43(C), pages 234-241.
  17. Ata, Rasit, 2015. "Artificial neural networks applications in wind energy systems: a review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 49(C), pages 534-562.
  18. Samuel Atuahene & Yukun Bao & Yao Yevenyo Ziggah & Patricia Semwaah Gyan & Feng Li, 2018. "Short-Term Electric Power Forecasting Using Dual-Stage Hierarchical Wavelet- Particle Swarm Optimization- Adaptive Neuro-Fuzzy Inference System PSO-ANFIS Approach Based On Climate Change," Energies, MDPI, vol. 11(10), pages 1-19, October.
  19. Akçay, Hüseyin & Filik, Tansu, 2017. "Short-term wind speed forecasting by spectral analysis from long-term observations with missing values," Applied Energy, Elsevier, vol. 191(C), pages 653-662.
  20. De Giorgi, Maria Grazia & Ficarella, Antonio & Tarantino, Marco, 2011. "Assessment of the benefits of numerical weather predictions in wind power forecasting based on statistical methods," Energy, Elsevier, vol. 36(7), pages 3968-3978.
  21. Honglu Zhu & Xu Li & Qiao Sun & Ling Nie & Jianxi Yao & Gang Zhao, 2015. "A Power Prediction Method for Photovoltaic Power Plant Based on Wavelet Decomposition and Artificial Neural Networks," Energies, MDPI, vol. 9(1), pages 1-15, December.
  22. Syed Muhammad Mohsin & Tahir Maqsood & Sajjad Ahmed Madani, 2022. "Solar and Wind Energy Forecasting for Green and Intelligent Migration of Traditional Energy Sources," Sustainability, MDPI, vol. 14(23), pages 1-20, December.
  23. Jung, Jaesung & Broadwater, Robert P., 2014. "Current status and future advances for wind speed and power forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 31(C), pages 762-777.
  24. Hu, Jianming & Wang, Jianzhou & Ma, Kailiang, 2015. "A hybrid technique for short-term wind speed prediction," Energy, Elsevier, vol. 81(C), pages 563-574.
  25. Wang, Jian-Zhou & Wang, Yun & Jiang, Ping, 2015. "The study and application of a novel hybrid forecasting model – A case study of wind speed forecasting in China," Applied Energy, Elsevier, vol. 143(C), pages 472-488.
  26. Jinling Lu & Bo Wang & Hui Ren & Daqian Zhao & Fei Wang & Miadreza Shafie-khah & João P. S. Catalão, 2017. "Two-Tier Reactive Power and Voltage Control Strategy Based on ARMA Renewable Power Forecasting Models," Energies, MDPI, vol. 10(10), pages 1-13, October.
  27. Lazić, Lazar & Pejanović, Goran & Živković, Momčilo & Ilić, Luka, 2014. "Improved wind forecasts for wind power generation using the Eta model and MOS (Model Output Statistics) method," Energy, Elsevier, vol. 73(C), pages 567-574.
  28. Yan, Jie & Liu, Yongqian & Han, Shuang & Qiu, Meng, 2013. "Wind power grouping forecasts and its uncertainty analysis using optimized relevance vector machine," Renewable and Sustainable Energy Reviews, Elsevier, vol. 27(C), pages 613-621.
  29. 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.
  30. Liu, Da & Niu, Dongxiao & Wang, Hui & Fan, Leilei, 2014. "Short-term wind speed forecasting using wavelet transform and support vector machines optimized by genetic algorithm," Renewable Energy, Elsevier, vol. 62(C), pages 592-597.
  31. Croonenbroeck, Carsten & Hüttel, Silke, 2017. "Quantifying the economic efficiency impact of inaccurate renewable energy price forecasts," Energy, Elsevier, vol. 134(C), pages 767-774.
  32. Hu, Weicheng & Yang, Qingshan & Chen, Hua-Peng & Yuan, Ziting & Li, Chen & Shao, Shuai & Zhang, Jian, 2021. "New hybrid approach for short-term wind speed predictions based on preprocessing algorithm and optimization theory," Renewable Energy, Elsevier, vol. 179(C), pages 2174-2186.
  33. Croonenbroeck, Carsten & Ambach, Daniel, 2015. "Censored spatial wind power prediction with random effects," Renewable and Sustainable Energy Reviews, Elsevier, vol. 51(C), pages 613-622.
  34. Marvuglia, Antonino & Messineo, Antonio, 2012. "Monitoring of wind farms’ power curves using machine learning techniques," Applied Energy, Elsevier, vol. 98(C), pages 574-583.
  35. Kriechbaumer, Thomas & Angus, Andrew & Parsons, David & Rivas Casado, Monica, 2014. "An improved wavelet–ARIMA approach for forecasting metal prices," Resources Policy, Elsevier, vol. 39(C), pages 32-41.
  36. Luis Lopez & Ingrid Oliveros & Luis Torres & Lacides Ripoll & Jose Soto & Giovanny Salazar & Santiago Cantillo, 2020. "Prediction of Wind Speed Using Hybrid Techniques," Energies, MDPI, vol. 13(23), pages 1-13, November.
  37. Liu, Hui & Tian, Hong-qi & Li, Yan-fei, 2012. "Comparison of two new ARIMA-ANN and ARIMA-Kalman hybrid methods for wind speed prediction," Applied Energy, Elsevier, vol. 98(C), pages 415-424.
  38. Mohammad Abdul Baseer & Anas Almunif & Ibrahim Alsaduni & Nazia Tazeen, 2023. "Electrical Power Generation Forecasting from Renewable Energy Systems Using Artificial Intelligence Techniques," Energies, MDPI, vol. 16(18), pages 1-21, September.
  39. Sun, Gaiping & Jiang, Chuanwen & Cheng, Pan & Liu, Yangyang & Wang, Xu & Fu, Yang & He, Yang, 2018. "Short-term wind power forecasts by a synthetical similar time series data mining method," Renewable Energy, Elsevier, vol. 115(C), pages 575-584.
  40. Rodrigues, R.B. & Mendes, V.M.F. & Catalão, J.P.S., 2011. "Protection of wind energy systems against the indirect effects of lightning," Renewable Energy, Elsevier, vol. 36(11), pages 2888-2896.
  41. Herbert Amezquita & Pedro M. S. Carvalho & Hugo Morais, 2023. "Wind Forecast at Medium Voltage Distribution Networks," Energies, MDPI, vol. 16(6), pages 1-23, March.
  42. Amir Abdul Majid, 2022. "Forecasting Monthly Wind Energy Using an Alternative Machine Training Method with Curve Fitting and Temporal Error Extraction Algorithm," Energies, MDPI, vol. 15(22), pages 1-24, November.
  43. Wang, Cong & Zhang, Hongli & Fan, Wenhui & Fan, Xiaochao, 2016. "A new wind power prediction method based on chaotic theory and Bernstein Neural Network," Energy, Elsevier, vol. 117(P1), pages 259-271.
  44. Tascikaraoglu, A. & Uzunoglu, M. & Vural, B., 2012. "The assessment of the contribution of short-term wind power predictions to the efficiency of stand-alone hybrid systems," Applied Energy, Elsevier, vol. 94(C), pages 156-165.
  45. Yu, Min & Niu, Dongxiao & Gao, Tian & Wang, Keke & Sun, Lijie & Li, Mingyu & Xu, Xiaomin, 2023. "A novel framework for ultra-short-term interval wind power prediction based on RF-WOA-VMD and BiGRU optimized by the attention mechanism," Energy, Elsevier, vol. 269(C).
  46. Khathutshelo Steven Sivhugwana & Edmore Ranganai, 2025. "Wind Speed Forecasting with Differentially Evolved Minimum-Bandwidth Filters and Gated Recurrent Units," Forecasting, MDPI, vol. 7(2), pages 1-27, June.
  47. Qian, Zheng & Pei, Yan & Zareipour, Hamidreza & Chen, Niya, 2019. "A review and discussion of decomposition-based hybrid models for wind energy forecasting applications," Applied Energy, Elsevier, vol. 235(C), pages 939-953.
  48. Zhongkai Feng & Wenjing Niu & Sen Wang & Chuntian Cheng & Zhenguo Song, 2019. "Mixed Integer Linear Programming Model for Peak Operation of Gas-Fired Generating Units with Disjoint-Prohibited Operating Zones," Energies, MDPI, vol. 12(11), pages 1-17, June.
  49. Liu, Da & Wang, Jilong & Wang, Hui, 2015. "Short-term wind speed forecasting based on spectral clustering and optimised echo state networks," Renewable Energy, Elsevier, vol. 78(C), pages 599-608.
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