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Wind Speed Prediction Using a Univariate ARIMA Model and a Multivariate NARX Model

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Cited by:

  1. Athraa Ali Kadhem & Noor Izzri Abdul Wahab & Ishak Aris & Jasronita Jasni & Ahmed N. Abdalla, 2017. "Advanced Wind Speed Prediction Model Based on a Combination of Weibull Distribution and an Artificial Neural Network," Energies, MDPI, vol. 10(11), pages 1-17, October.
  2. Mohammad Mahdi Forootan & Iman Larki & Rahim Zahedi & Abolfazl Ahmadi, 2022. "Machine Learning and Deep Learning in Energy Systems: A Review," Sustainability, MDPI, vol. 14(8), pages 1-49, April.
  3. So-Kumneth Sim & Philipp Maass & Pedro G. Lind, 2018. "Wind Speed Modeling by Nested ARIMA Processes," Energies, MDPI, vol. 12(1), pages 1-18, December.
  4. Ahmed, Adil & Khalid, Muhammad, 2018. "An intelligent framework for short-term multi-step wind speed forecasting based on Functional Networks," Applied Energy, Elsevier, vol. 225(C), pages 902-911.
  5. Ahmed, Adil & Khalid, Muhammad, 2019. "A review on the selected applications of forecasting models in renewable power systems," Renewable and Sustainable Energy Reviews, Elsevier, vol. 100(C), pages 9-21.
  6. Wang, Qiang & Li, Shuyu & Li, Rongrong & Ma, Minglu, 2018. "Forecasting U.S. shale gas monthly production using a hybrid ARIMA and metabolic nonlinear grey model," Energy, Elsevier, vol. 160(C), pages 378-387.
  7. Serin, Faruk & Alisan, Yigit & Kece, Adnan, 2021. "Hybrid time series forecasting methods for travel time prediction," Physica A: Statistical Mechanics and its Applications, Elsevier, vol. 579(C).
  8. Bórawski, Piotr & Bełdycka-Bórawska, Aneta & Jankowski, Krzysztof Jóżef & Dubis, Bogdan & Dunn, James W., 2020. "Development of wind energy market in the European Union," Renewable Energy, Elsevier, vol. 161(C), pages 691-700.
  9. Hufang Yang & Zaiping Jiang & Haiyan Lu, 2017. "A Hybrid Wind Speed Forecasting System Based on a ‘Decomposition and Ensemble’ Strategy and Fuzzy Time Series," Energies, MDPI, vol. 10(9), pages 1-30, September.
  10. Aqdas Naz & Nadeem Javaid & Muhammad Babar Rasheed & Abdul Haseeb & Musaed Alhussein & Khursheed Aurangzeb, 2019. "Game Theoretical Energy Management with Storage Capacity Optimization and Photo-Voltaic Cell Generated Power Forecasting in Micro Grid," Sustainability, MDPI, vol. 11(10), pages 1-22, May.
  11. Antonello Rosato & Rodolfo Araneo & Amedeo Andreotti & Federico Succetti & Massimo Panella, 2021. "2-D Convolutional Deep Neural Network for the Multivariate Prediction of Photovoltaic Time Series," Energies, MDPI, vol. 14(9), pages 1-18, April.
  12. Mostafa Farrokhabadi, 2019. "Data-Driven Mitigation of Energy Scheduling Inaccuracy in Renewable-Penetrated Grids: Summerside Electric Use Case," Energies, MDPI, vol. 12(12), pages 1-23, June.
  13. Ahmed, R. & Sreeram, V. & Mishra, Y. & Arif, M.D., 2020. "A review and evaluation of the state-of-the-art in PV solar power forecasting: Techniques and optimization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 124(C).
  14. Neeraj Bokde & Andrés Feijóo & Nadhir Al-Ansari & Siyu Tao & Zaher Mundher Yaseen, 2020. "The Hybridization of Ensemble Empirical Mode Decomposition with Forecasting Models: Application of Short-Term Wind Speed and Power Modeling," Energies, MDPI, vol. 13(7), pages 1-23, April.
  15. Vladimir Simankov & Pavel Buchatskiy & Semen Teploukhov & Stefan Onishchenko & Anatoliy Kazak & Petr Chetyrbok, 2023. "Review of Estimating and Predicting Models of the Wind Energy Amount," Energies, MDPI, vol. 16(16), pages 1-24, August.
  16. Xin Zhao & Haikun Wei & Chenxi Li & Kanjian Zhang, 2020. "A Hybrid Nonlinear Forecasting Strategy for Short-Term Wind Speed," Energies, MDPI, vol. 13(7), pages 1-15, April.
  17. Karimi, Sajad & Kwon, Soongeol, 2022. "Optimization-driven uncertainty forecasting: Application to day-ahead commitment with renewable energy resources," Applied Energy, Elsevier, vol. 326(C).
  18. Nakıp, Mert & Çopur, Onur & Biyik, Emrah & Güzeliş, Cüneyt, 2023. "Renewable energy management in smart home environment via forecast embedded scheduling based on Recurrent Trend Predictive Neural Network," Applied Energy, Elsevier, vol. 340(C).
  19. Pataro, Igor M.L. & Gil, Juan D. & Guzmán, José L. & Berenguel, Manuel & Lemos, João M., 2023. "Hierarchical control based on a hybrid nonlinear predictive strategy for a solar-powered absorption machine facility," Energy, Elsevier, vol. 271(C).
  20. Yang, Zhongshan & Wang, Jian, 2018. "A combination forecasting approach applied in multistep wind speed forecasting based on a data processing strategy and an optimized artificial intelligence algorithm," Applied Energy, Elsevier, vol. 230(C), pages 1108-1125.
  21. Zina Boussaada & Octavian Curea & Ahmed Remaci & Haritza Camblong & Najiba Mrabet Bellaaj, 2018. "A Nonlinear Autoregressive Exogenous (NARX) Neural Network Model for the Prediction of the Daily Direct Solar Radiation," Energies, MDPI, vol. 11(3), pages 1-21, March.
  22. Majidi Nezhad, M. & Heydari, A. & Pirshayan, E. & Groppi, D. & Astiaso Garcia, D., 2021. "A novel forecasting model for wind speed assessment using sentinel family satellites images and machine learning method," Renewable Energy, Elsevier, vol. 179(C), pages 2198-2211.
  23. Erick López & Carlos Valle & Héctor Allende & Esteban Gil & Henrik Madsen, 2018. "Wind Power Forecasting Based on Echo State Networks and Long Short-Term Memory," Energies, MDPI, vol. 11(3), pages 1-22, February.
  24. Zhiwei Li & Tianran Jin & Shuqiang Zhao & Jinshan Liu, 2018. "Power System Day-Ahead Unit Commitment Based on Chance-Constrained Dependent Chance Goal Programming," Energies, MDPI, vol. 11(7), pages 1-20, July.
  25. Zucatelli, P.J. & Nascimento, E.G.S. & Santos, A.Á.B. & Arce, A.M.G. & Moreira, D.M., 2021. "An investigation on deep learning and wavelet transform to nowcast wind power and wind power ramp: A case study in Brazil and Uruguay," Energy, Elsevier, vol. 230(C).
  26. Natei Ermias Benti & Mesfin Diro Chaka & Addisu Gezahegn Semie, 2023. "Forecasting Renewable Energy Generation with Machine Learning and Deep Learning: Current Advances and Future Prospects," Sustainability, MDPI, vol. 15(9), pages 1-33, April.
  27. Jorge Barrientos Marin & Elkin Tabares Orozco & Esteban Velilla, 2018. "Forecasting electricity price in Colombia: A comparison between Neural Network, ARMA process and Hybrid Models," International Journal of Energy Economics and Policy, Econjournals, vol. 8(3), pages 97-106.
  28. Yiqi Chu & Chengcai Li & Yefang Wang & Jing Li & Jian Li, 2016. "A Long-Term Wind Speed Ensemble Forecasting System with Weather Adapted Correction," Energies, MDPI, vol. 9(11), pages 1-20, October.
  29. Nantian Huang & Chong Yuan & Guowei Cai & Enkai Xing, 2016. "Hybrid Short Term Wind Speed Forecasting Using Variational Mode Decomposition and a Weighted Regularized Extreme Learning Machine," Energies, MDPI, vol. 9(12), pages 1-19, November.
  30. Dai, Baorui & Xia, Ye & Li, Qi, 2022. "An extreme value prediction method based on clustering algorithm," Reliability Engineering and System Safety, Elsevier, vol. 222(C).
  31. Ilias G. Marneris & Pandelis N. Biskas & Anastasios G. Bakirtzis, 2017. "Stochastic and Deterministic Unit Commitment Considering Uncertainty and Variability Reserves for High Renewable Integration," Energies, MDPI, vol. 10(1), pages 1-25, January.
  32. Saber Talari & Miadreza Shafie-khah & Gerardo J. Osório & Fei Wang & Alireza Heidari & João P. S. Catalão, 2017. "Price Forecasting of Electricity Markets in the Presence of a High Penetration of Wind Power Generators," Sustainability, MDPI, vol. 9(11), pages 1-13, November.
  33. Dalton, Amaris & Bekker, Bernard, 2022. "Exogenous atmospheric variables as wind speed predictors in machine learning," Applied Energy, Elsevier, vol. 319(C).
  34. Baruque, Bruno & Porras, Santiago & Jove, Esteban & Calvo-Rolle, José Luis, 2019. "Geothermal heat exchanger energy prediction based on time series and monitoring sensors optimization," Energy, Elsevier, vol. 171(C), pages 49-60.
  35. Cai, Kaiquan & Shen, Zhiqi & Luo, Xiaoyan & Li, Yue, 2023. "Temporal attention aware dual-graph convolution network for air traffic flow prediction," Journal of Air Transport Management, Elsevier, vol. 106(C).
  36. Vadim Manusov & Pavel Matrenin & Muso Nazarov & Svetlana Beryozkina & Murodbek Safaraliev & Inga Zicmane & Anvari Ghulomzoda, 2023. "Short-Term Prediction of the Wind Speed Based on a Learning Process Control Algorithm in Isolated Power Systems," Sustainability, MDPI, vol. 15(2), pages 1-12, January.
  37. Hamidreza Mirtaheri & Piero Macaluso & Maurizio Fantino & Marily Efstratiadi & Sotiris Tsakanikas & Panagiotis Papadopoulos & Andrea Mazza, 2021. "Hybrid Forecast and Control Chain for Operation of Flexibility Assets in Micro-Grids," Energies, MDPI, vol. 14(21), pages 1-22, November.
  38. Cheng-Yu Ho & Ke-Sheng Cheng & Chi-Hang Ang, 2023. "Utilizing the Random Forest Method for Short-Term Wind Speed Forecasting in the Coastal Area of Central Taiwan," Energies, MDPI, vol. 16(3), pages 1-18, January.
  39. Geovanny Marulanda & Antonio Bello & Jenny Cifuentes & Javier Reneses, 2020. "Wind Power Long-Term Scenario Generation Considering Spatial-Temporal Dependencies in Coupled Electricity Markets," Energies, MDPI, vol. 13(13), pages 1-19, July.
  40. Neeraj Bokde & Andrés Feijóo & Daniel Villanueva & Kishore Kulat, 2018. "A Novel and Alternative Approach for Direct and Indirect Wind-Power Prediction Methods," Energies, MDPI, vol. 11(11), pages 1-19, October.
  41. Wang, Qiang & Jiang, Feng, 2019. "Integrating linear and nonlinear forecasting techniques based on grey theory and artificial intelligence to forecast shale gas monthly production in Pennsylvania and Texas of the United States," Energy, Elsevier, vol. 178(C), pages 781-803.
  42. Caputo, Antonio C. & Federici, Alessandro & Pelagagge, Pacifico M. & Salini, Paolo, 2023. "Offshore wind power system economic evaluation framework under aleatory and epistemic uncertainty," Applied Energy, Elsevier, vol. 350(C).
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