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Recurrent Neural Network-Based Hourly Prediction of Photovoltaic Power Output Using Meteorological Information

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Cited by:

  1. Evgeny Solomin & Shanmuga Priya Selvanathan & Sudhakar Kumarasamy & Anton Kovalyov & Ramyashree Maddappa Srinivasa, 2021. "The Comparison of Solar-Powered Hydrogen Closed-Cycle System Capacities for Selected Locations," Energies, MDPI, vol. 14(9), pages 1-18, May.
  2. Promphak Dawan & Kobsak Sriprapha & Songkiate Kittisontirak & Terapong Boonraksa & Nitikorn Junhuathon & Wisut Titiroongruang & Surasak Niemcharoen, 2020. "Comparison of Power Output Forecasting on the Photovoltaic System Using Adaptive Neuro-Fuzzy Inference Systems and Particle Swarm Optimization-Artificial Neural Network Model," Energies, MDPI, vol. 13(2), pages 1-18, January.
  3. Gülsah Erdogan & Wiem Fekih Hassen, 2023. "Charging Scheduling of Hybrid Energy Storage Systems for EV Charging Stations," Energies, MDPI, vol. 16(18), pages 1-29, September.
  4. Ahmad Manasrah & Mohammad Masoud & Yousef Jaradat & Piero Bevilacqua, 2022. "Investigation of a Real-Time Dynamic Model for a PV Cooling System," Energies, MDPI, vol. 15(5), pages 1-15, March.
  5. Abdul Rauf Bhatti & Ahmed Bilal Awan & Walied Alharbi & Zainal Salam & Abdullah S. Bin Humayd & Praveen R. P. & Kankar Bhattacharya, 2021. "An Improved Approach to Enhance Training Performance of ANN and the Prediction of PV Power for Any Time-Span without the Presence of Real-Time Weather Data," Sustainability, MDPI, vol. 13(21), pages 1-18, October.
  6. du Plessis, A.A. & Strauss, J.M. & Rix, A.J., 2021. "Short-term solar power forecasting: Investigating the ability of deep learning models to capture low-level utility-scale Photovoltaic system behaviour," Applied Energy, Elsevier, vol. 285(C).
  7. Grzegorz Drałus & Damian Mazur & Jacek Kusznier & Jakub Drałus, 2023. "Application of Artificial Intelligence Algorithms in Multilayer Perceptron and Elman Networks to Predict Photovoltaic Power Plant Generation," Energies, MDPI, vol. 16(18), pages 1-23, September.
  8. Simon Liebermann & Jung-Sup Um & YoungSeok Hwang & Stephan Schlüter, 2021. "Performance Evaluation of Neural Network-Based Short-Term Solar Irradiation Forecasts," Energies, MDPI, vol. 14(11), pages 1-21, May.
  9. 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.
  10. Li, Naiqing & Li, Longhao & Zhang, Fan & Jiao, Ticao & Wang, Shuang & Liu, Xuefeng & Wu, Xinghua, 2023. "Research on short-term photovoltaic power prediction based on multi-scale similar days and ESN-KELM dual core prediction model," Energy, Elsevier, vol. 277(C).
  11. Aleksandar Dimovski & Matteo Moncecchi & Davide Falabretti & Marco Merlo, 2020. "PV Forecast for the Optimal Operation of the Medium Voltage Distribution Network: A Real-Life Implementation on a Large Scale Pilot," Energies, MDPI, vol. 13(20), pages 1-21, October.
  12. Guilherme Henrique Alves & Geraldo Caixeta Guimarães & Fabricio Augusto Matheus Moura, 2023. "Battery Storage Systems Control Strategies with Intelligent Algorithms in Microgrids with Dynamic Pricing," Energies, MDPI, vol. 16(14), pages 1-30, July.
  13. Chih-Hong Lin, 2020. "Permanent-Magnet Synchronous Motor Drive System Using Backstepping Control with Three Adaptive Rules and Revised Recurring Sieved Pollaczek Polynomials Neural Network with Reformed Grey Wolf Optimizat," Energies, MDPI, vol. 13(22), pages 1-33, November.
  14. Ana Carolina do Amaral Burghi & Tobias Hirsch & Robert Pitz-Paal, 2020. "Artificial Learning Dispatch Planning with Probabilistic Forecasts: Using Uncertainties as an Asset," Energies, MDPI, vol. 13(3), pages 1-25, February.
  15. Wenting Zhao & Juanjuan Zhao & Xilong Yao & Zhixin Jin & Pan Wang, 2019. "A Novel Adaptive Intelligent Ensemble Model for Forecasting Primary Energy Demand," Energies, MDPI, vol. 12(7), pages 1-28, April.
  16. Der-Fa Chen & Yi-Cheng Shih & Shih-Cheng Li & Chin-Tung Chen & Jung-Chu Ting, 2020. "Permanent-Magnet SLM Drive System Using AMRRSPNNB Control System with DGWO," Energies, MDPI, vol. 13(11), pages 1-25, June.
  17. Sabadus, Andreea & Blaga, Robert & Hategan, Sergiu-Mihai & Calinoiu, Delia & Paulescu, Eugenia & Mares, Oana & Boata, Remus & Stefu, Nicoleta & Paulescu, Marius & Badescu, Viorel, 2024. "A cross-sectional survey of deterministic PV power forecasting: Progress and limitations in current approaches," Renewable Energy, Elsevier, vol. 226(C).
  18. Thi Ngoc Nguyen & Felix Musgens, 2021. "What drives the accuracy of PV output forecasts?," Papers 2111.02092, arXiv.org.
  19. Donghun Lee & Seok Mann Yoon & Jaeseung Lee & Kwanho Kim & Sang Hwa Song, 2020. "Applying Deep Learning to the Heat Production Planning Problem in a District Heating System," Energies, MDPI, vol. 13(24), pages 1-17, December.
  20. Alfredo Nespoli & Emanuele Ogliari & Sonia Leva & Alessandro Massi Pavan & Adel Mellit & Vanni Lughi & Alberto Dolara, 2019. "Day-Ahead Photovoltaic Forecasting: A Comparison of the Most Effective Techniques," Energies, MDPI, vol. 12(9), pages 1-15, April.
  21. Kim, Jangkyum & Oh, Hyeontaek & Choi, Jun Kyun, 2022. "Learning based cost optimal energy management model for campus microgrid systems," Applied Energy, Elsevier, vol. 311(C).
  22. Victor Hugo Wentz & Joylan Nunes Maciel & Jorge Javier Gimenez Ledesma & Oswaldo Hideo Ando Junior, 2022. "Solar Irradiance Forecasting to Short-Term PV Power: Accuracy Comparison of ANN and LSTM Models," Energies, MDPI, vol. 15(7), pages 1-23, March.
  23. Putri Nor Liyana Mohamad Radzi & Muhammad Naveed Akhter & Saad Mekhilef & Noraisyah Mohamed Shah, 2023. "Review on the Application of Photovoltaic Forecasting Using Machine Learning for Very Short- to Long-Term Forecasting," Sustainability, MDPI, vol. 15(4), pages 1-21, February.
  24. Rial A. Rajagukguk & Raden A. A. Ramadhan & Hyun-Jin Lee, 2020. "A Review on Deep Learning Models for Forecasting Time Series Data of Solar Irradiance and Photovoltaic Power," Energies, MDPI, vol. 13(24), pages 1-23, December.
  25. 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.
  26. Nguyen, Thi Ngoc & Müsgens, Felix, 2022. "What drives the accuracy of PV output forecasts?," Applied Energy, Elsevier, vol. 323(C).
  27. Abdil Karakan, 2024. "Predicting Energy Production in Renewable Energy Power Plants Using Deep Learning," Energies, MDPI, vol. 17(16), pages 1-13, August.
  28. Franko Pandžić & Tomislav Capuder, 2023. "Advances in Short-Term Solar Forecasting: A Review and Benchmark of Machine Learning Methods and Relevant Data Sources," Energies, MDPI, vol. 17(1), pages 1-19, December.
  29. Barja-Martinez, Sara & Aragüés-Peñalba, Mònica & Munné-Collado, Íngrid & Lloret-Gallego, Pau & Bullich-Massagué, Eduard & Villafafila-Robles, Roberto, 2021. "Artificial intelligence techniques for enabling Big Data services in distribution networks: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 150(C).
  30. N. Yogambal Jayalakshmi & R. Shankar & Umashankar Subramaniam & I. Baranilingesan & Alagar Karthick & Balasubramaniam Stalin & Robbi Rahim & Aritra Ghosh, 2021. "Novel Multi-Time Scale Deep Learning Algorithm for Solar Irradiance Forecasting," Energies, MDPI, vol. 14(9), pages 1-23, April.
  31. Linh Bui Duy & Ninh Nguyen Quang & Binh Doan Van & Eleonora Riva Sanseverino & Quynh Tran Thi Tu & Hang Le Thi Thuy & Sang Le Quang & Thinh Le Cong & Huyen Cu Thi Thanh, 2024. "Refining Long Short-Term Memory Neural Network Input Parameters for Enhanced Solar Power Forecasting," Energies, MDPI, vol. 17(16), pages 1-22, August.
  32. Michael Wood & Emanuele Ogliari & Alfredo Nespoli & Travis Simpkins & Sonia Leva, 2023. "Day Ahead Electric Load Forecast: A Comprehensive LSTM-EMD Methodology and Several Diverse Case Studies," Forecasting, MDPI, vol. 5(1), pages 1-18, March.
  33. Sankara kumar, Sundarapandian & Karthick, Alagar & Shankar, R. & Dharmaraj, Ganeshaperumal, 2024. "Energy forecasting of the building integrated photovoltaic system based on deep learning dragonfly-firefly algorithm," Energy, Elsevier, vol. 308(C).
  34. Shree Krishna Acharya & Young-Min Wi & Jaehee Lee, 2021. "Weather Data Mixing Models for Day-Ahead PV Forecasting in Small-Scale PV Plants," Energies, MDPI, vol. 14(11), pages 1-16, May.
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