IDEAS home Printed from https://ideas.repec.org/r/eee/renene/v66y2014icp78-89.html
   My bibliography  Save this item

An ARMAX model for forecasting the power output of a grid connected photovoltaic system

Citations

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


Cited by:

  1. Dukhwan Yu & Wonik Choi & Myoungsoo Kim & Ling Liu, 2020. "Forecasting Day-Ahead Hourly Photovoltaic Power Generation Using Convolutional Self-Attention Based Long Short-Term Memory," Energies, MDPI, vol. 13(15), pages 1-17, August.
  2. Trigo-González, Mauricio & Batlles, F.J. & Alonso-Montesinos, Joaquín & Ferrada, Pablo & del Sagrado, J. & Martínez-Durbán, M. & Cortés, Marcelo & Portillo, Carlos & Marzo, Aitor, 2019. "Hourly PV production estimation by means of an exportable multiple linear regression model," Renewable Energy, Elsevier, vol. 135(C), pages 303-312.
  3. Qu, Yinpeng & Xu, Jian & Sun, Yuanzhang & Liu, Dan, 2021. "A temporal distributed hybrid deep learning model for day-ahead distributed PV power forecasting," Applied Energy, Elsevier, vol. 304(C).
  4. Yin, Wansi & Han, Yutong & Zhou, Hai & Ma, Ming & Li, Li & Zhu, Honglu, 2020. "A novel non-iterative correction method for short-term photovoltaic power forecasting," Renewable Energy, Elsevier, vol. 159(C), pages 23-32.
  5. Miaomiao Niu & Guohao Li, 2022. "The Impact of Climate Change Risks on Residential Consumption in China: Evidence from ARMAX Modeling and Granger Causality Analysis," IJERPH, MDPI, vol. 19(19), pages 1-15, September.
  6. Chia-Sheng Tu & Wen-Chang Tsai & Chih-Ming Hong & Whei-Min Lin, 2022. "Short-Term Solar Power Forecasting via General Regression Neural Network with Grey Wolf Optimization," Energies, MDPI, vol. 15(18), pages 1-20, September.
  7. Lee, Donghun & Kim, Kwanho, 2021. "PV power prediction in a peak zone using recurrent neural networks in the absence of future meteorological information," Renewable Energy, Elsevier, vol. 173(C), pages 1098-1110.
  8. Ling-Ling Li & Shi-Yu Wen & Ming-Lang Tseng & Anthony S. F. Chiu, 2020. "Photovoltaic array prediction on short-term output power method in Centralized power generation system," Annals of Operations Research, Springer, vol. 290(1), pages 243-263, July.
  9. Li, Yanting & He, Yong & Su, Yan & Shu, Lianjie, 2016. "Forecasting the daily power output of a grid-connected photovoltaic system based on multivariate adaptive regression splines," Applied Energy, Elsevier, vol. 180(C), pages 392-401.
  10. Wang, Guochang & Su, Yan & Shu, Lianjie, 2016. "One-day-ahead daily power forecasting of photovoltaic systems based on partial functional linear regression models," Renewable Energy, Elsevier, vol. 96(PA), pages 469-478.
  11. Konduru Sudharshan & C. Naveen & Pradeep Vishnuram & Damodhara Venkata Siva Krishna Rao Kasagani & Benedetto Nastasi, 2022. "Systematic Review on Impact of Different Irradiance Forecasting Techniques for Solar Energy Prediction," Energies, MDPI, vol. 15(17), pages 1-39, August.
  12. Mariz B. Arias & Sungwoo Bae, 2020. "Design Models for Power Flow Management of a Grid-Connected Solar Photovoltaic System with Energy Storage System," Energies, MDPI, vol. 13(9), pages 1-14, April.
  13. Chao Huang & Longpeng Cao & Nanxin Peng & Sijia Li & Jing Zhang & Long Wang & Xiong Luo & Jenq-Haur Wang, 2018. "Day-Ahead Forecasting of Hourly Photovoltaic Power Based on Robust Multilayer Perception," Sustainability, MDPI, vol. 10(12), pages 1-8, December.
  14. Paulescu, Marius & Blaga, Robert & Dughir, Ciprian & Stefu, Nicoleta & Sabadus, Andreea & Calinoiu, Delia & Badescu, Viorel, 2023. "Intra-hour PV power forecasting based on sky imagery," Energy, Elsevier, vol. 279(C).
  15. Sergio Cantillo-Luna & Ricardo Moreno-Chuquen & David Celeita & George Anders, 2023. "Deep and Machine Learning Models to Forecast Photovoltaic Power Generation," Energies, MDPI, vol. 16(10), pages 1-24, May.
  16. Andi A. H. Lateko & Hong-Tzer Yang & Chao-Ming Huang & Happy Aprillia & Che-Yuan Hsu & Jie-Lun Zhong & Nguyễn H. Phương, 2021. "Stacking Ensemble Method with the RNN Meta-Learner for Short-Term PV Power Forecasting," Energies, MDPI, vol. 14(16), pages 1-23, August.
  17. Ze Wu & Feifan Pan & Dandan Li & Hao He & Tiancheng Zhang & Shuyun Yang, 2022. "Prediction of Photovoltaic Power by the Informer Model Based on Convolutional Neural Network," Sustainability, MDPI, vol. 14(20), pages 1-16, October.
  18. Elena Collino & Dario Ronzio, 2021. "Exploitation of a New Short-Term Multimodel Photovoltaic Power Forecasting Method in the Very Short-Term Horizon to Derive a Multi-Time Scale Forecasting System," Energies, MDPI, vol. 14(3), pages 1-30, February.
  19. Seung Chan Jo & Young Gyu Jin & Yong Tae Yoon & Ho Chan Kim, 2021. "Methods for Integrating Extraterrestrial Radiation into Neural Network Models for Day-Ahead PV Generation Forecasting," Energies, MDPI, vol. 14(9), pages 1-18, May.
  20. Damilola A. Asaleye & Michael Breen & Michael D. Murphy, 2017. "A Decision Support Tool for Building Integrated Renewable Energy Microgrids Connected to a Smart Grid," Energies, MDPI, vol. 10(11), pages 1-29, November.
  21. Mohamed Massaoudi & Ines Chihi & Lilia Sidhom & Mohamed Trabelsi & Shady S. Refaat & Fakhreddine S. Oueslati, 2021. "Enhanced Random Forest Model for Robust Short-Term Photovoltaic Power Forecasting Using Weather Measurements," Energies, MDPI, vol. 14(13), pages 1-20, July.
  22. Kaitong Wu & Xiangang Peng & Zilu Li & Wenbo Cui & Haoliang Yuan & Chun Sing Lai & Loi Lei Lai, 2022. "A Short-Term Photovoltaic Power Forecasting Method Combining a Deep Learning Model with Trend Feature Extraction and Feature Selection," Energies, MDPI, vol. 15(15), pages 1-20, July.
  23. Bórawski, Piotr & Holden, Lisa & Bełdycka-Bórawska, Aneta, 2023. "Perspectives of photovoltaic energy market development in the european union," Energy, Elsevier, vol. 270(C).
  24. Zhuoyuan Lyu & Ying Shen & Yu Zhao & Tao Hu, 2023. "Solar Radiation Prediction Based on Conformer-GLaplace-SDAR Model," Sustainability, MDPI, vol. 15(20), pages 1-18, October.
  25. Bikrant Kesari & Sunil Atulkar & Satyanarayan Pandey, 2021. "Consumer Purchasing Behaviour towards Eco-Environment Residential Photovoltaic Solar Lighting Systems," Global Business Review, International Management Institute, vol. 22(1), pages 236-254, February.
  26. Luo, Xing & Zhang, Dongxiao, 2023. "A cascaded deep learning framework for photovoltaic power forecasting with multi-fidelity inputs," Energy, Elsevier, vol. 268(C).
  27. Hassan, Muhammed A. & Bailek, Nadjem & Bouchouicha, Kada & Nwokolo, Samuel Chukwujindu, 2021. "Ultra-short-term exogenous forecasting of photovoltaic power production using genetically optimized non-linear auto-regressive recurrent neural networks," Renewable Energy, Elsevier, vol. 171(C), pages 191-209.
  28. Yadav, Amit Kumar & Sharma, Vikrant & Malik, Hasmat & Chandel, S.S., 2018. "Daily array yield prediction of grid-interactive photovoltaic plant using relief attribute evaluator based Radial Basis Function Neural Network," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P2), pages 2115-2127.
  29. De Giorgi, M.G. & Malvoni, M. & Congedo, P.M., 2016. "Comparison of strategies for multi-step ahead photovoltaic power forecasting models based on hybrid group method of data handling networks and least square support vector machine," Energy, Elsevier, vol. 107(C), pages 360-373.
  30. Bin Ma & Penghui Li & Xing Guo & Hongxue Zhao & Yong Chen, 2023. "A Novel Online Prediction Method for Vehicle Velocity and Road Gradient Based on a Flexible-Structure Auto-Regressive Integrated Moving Average Model," Sustainability, MDPI, vol. 15(21), pages 1-18, November.
  31. Kuk Yeol Bae & Han Seung Jang & Bang Chul Jung & Dan Keun Sung, 2019. "Effect of Prediction Error of Machine Learning Schemes on Photovoltaic Power Trading Based on Energy Storage Systems," Energies, MDPI, vol. 12(7), pages 1-20, April.
  32. Sen Wang & Yonghui Sun & Yan Zhou & Rabea Jamil Mahfoud & Dongchen Hou, 2019. "A New Hybrid Short-Term Interval Forecasting of PV Output Power Based on EEMD-SE-RVM," Energies, MDPI, vol. 13(1), pages 1-17, December.
  33. Zheng, Lingwei & Liu, Zhaokun & Shen, Junnan & Wu, Chenxi, 2018. "Very short-term maximum Lyapunov exponent forecasting tool for distributed photovoltaic output," Applied Energy, Elsevier, vol. 229(C), pages 1128-1139.
  34. Hongchao Zhang & Tengteng Zhu, 2022. "Stacking Model for Photovoltaic-Power-Generation Prediction," Sustainability, MDPI, vol. 14(9), pages 1-16, May.
  35. Debnath, Kumar Biswajit & Mourshed, Monjur, 2018. "Forecasting methods in energy planning models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 88(C), pages 297-325.
  36. Shireen, Tahasin & Shao, Chenhui & Wang, Hui & Li, Jingjing & Zhang, Xi & Li, Mingyang, 2018. "Iterative multi-task learning for time-series modeling of solar panel PV outputs," Applied Energy, Elsevier, vol. 212(C), pages 654-662.
  37. Bisoi, Ranjeeta & Dash, Deepak Ranjan & Dash, P.K. & Tripathy, Lokanath, 2022. "An efficient robust optimized functional link broad learning system for solar irradiance prediction," Applied Energy, Elsevier, vol. 319(C).
  38. Yu, Min & Niu, Dongxiao & Wang, Keke & Du, Ruoyun & Yu, Xiaoyu & Sun, Lijie & Wang, Feiran, 2023. "Short-term photovoltaic power point-interval forecasting based on double-layer decomposition and WOA-BiLSTM-Attention and considering weather classification," Energy, Elsevier, vol. 275(C).
  39. Li, Chengdong & Zhou, Changgeng & Peng, Wei & Lv, Yisheng & Luo, Xin, 2020. "Accurate prediction of short-term photovoltaic power generation via a novel double-input-rule-modules stacked deep fuzzy method," Energy, Elsevier, vol. 212(C).
  40. Francesco Nicoletti & Piero Bevilacqua, 2024. "Hourly Photovoltaic Production Prediction Using Numerical Weather Data and Neural Networks for Solar Energy Decision Support," Energies, MDPI, vol. 17(2), pages 1-22, January.
  41. Stefano Massucco & Gabriele Mosaico & Matteo Saviozzi & Federico Silvestro, 2019. "A Hybrid Technique for Day-Ahead PV Generation Forecasting Using Clear-Sky Models or Ensemble of Artificial Neural Networks According to a Decision Tree Approach," Energies, MDPI, vol. 12(7), pages 1-21, April.
  42. Donghun Lee & Kwanho Kim, 2019. "Recurrent Neural Network-Based Hourly Prediction of Photovoltaic Power Output Using Meteorological Information," Energies, MDPI, vol. 12(2), pages 1-22, January.
  43. Widén, Joakim & Carpman, Nicole & Castellucci, Valeria & Lingfors, David & Olauson, Jon & Remouit, Flore & Bergkvist, Mikael & Grabbe, Mårten & Waters, Rafael, 2015. "Variability assessment and forecasting of renewables: A review for solar, wind, wave and tidal resources," Renewable and Sustainable Energy Reviews, Elsevier, vol. 44(C), pages 356-375.
  44. Kwak, Younghoon & Mun, Sun-Hye & Park, Chang-Dae & Lee, Sang-Moon & Huh, Jung-Ho, 2022. "Statistical analysis of power generation of semi-transparent photovoltaic (STPV) for diversity in building envelope design: A mock-up test by azimuth and tilt angles," Renewable Energy, Elsevier, vol. 188(C), pages 651-669.
  45. Mariz B. Arias & Sungwoo Bae, 2021. "Solar Photovoltaic Power Prediction Using Big Data Tools," Sustainability, MDPI, vol. 13(24), pages 1-19, December.
  46. Erdener, Burcin Cakir & Feng, Cong & Doubleday, Kate & Florita, Anthony & Hodge, Bri-Mathias, 2022. "A review of behind-the-meter solar forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 160(C).
  47. Wei Li & Hui Ren & Ping Chen & Yanyang Wang & Hailong Qi, 2020. "Key Operational Issues on the Integration of Large-Scale Solar Power Generation—A Literature Review," Energies, MDPI, vol. 13(22), pages 1-25, November.
  48. Luo, Xing & Zhang, Dongxiao & Zhu, Xu, 2021. "Deep learning based forecasting of photovoltaic power generation by incorporating domain knowledge," Energy, Elsevier, vol. 225(C).
  49. María Carmen Ruiz-Abellón & Luis Alfredo Fernández-Jiménez & Antonio Guillamón & Alberto Falces & Ana García-Garre & Antonio Gabaldón, 2019. "Integration of Demand Response and Short-Term Forecasting for the Management of Prosumers’ Demand and Generation," Energies, MDPI, vol. 13(1), pages 1-31, December.
  50. Larson, David P. & Nonnenmacher, Lukas & Coimbra, Carlos F.M., 2016. "Day-ahead forecasting of solar power output from photovoltaic plants in the American Southwest," Renewable Energy, Elsevier, vol. 91(C), pages 11-20.
  51. Mohammed A. Bou-Rabee & Muhammad Yasin Naz & Imad ED. Albalaa & Shaharin Anwar Sulaiman, 2022. "BiLSTM Network-Based Approach for Solar Irradiance Forecasting in Continental Climate Zones," Energies, MDPI, vol. 15(6), pages 1-12, March.
  52. Amrouche, Badia & Le Pivert, Xavier, 2014. "Artificial neural network based daily local forecasting for global solar radiation," Applied Energy, Elsevier, vol. 130(C), pages 333-341.
  53. Camelo, Henrique do Nascimento & Lucio, Paulo Sérgio & Leal Junior, João Bosco Verçosa & Carvalho, Paulo Cesar Marques de & Santos, Daniel von Glehn dos, 2018. "Innovative hybrid models for forecasting time series applied in wind generation based on the combination of time series models with artificial neural networks," Energy, Elsevier, vol. 151(C), pages 347-357.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.