IDEAS home Printed from https://ideas.repec.org/r/eee/appene/v157y2015icp95-110.html
   My bibliography  Save this item

An analog ensemble for short-term probabilistic solar power forecast

Citations

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


Cited by:

  1. Athanasios I. Salamanis & Georgia Xanthopoulou & Napoleon Bezas & Christos Timplalexis & Angelina D. Bintoudi & Lampros Zyglakis & Apostolos C. Tsolakis & Dimosthenis Ioannidis & Dionysios Kehagias & , 2020. "Benchmark Comparison of Analytical, Data-Based and Hybrid Models for Multi-Step Short-Term Photovoltaic Power Generation Forecasting," Energies, MDPI, vol. 13(22), pages 1-31, November.
  2. David, Mathieu & Luis, Mazorra Aguiar & Lauret, Philippe, 2018. "Comparison of intraday probabilistic forecasting of solar irradiance using only endogenous data," International Journal of Forecasting, Elsevier, vol. 34(3), pages 529-547.
  3. Lima, Marcello Anderson F.B. & Carvalho, Paulo C.M. & Fernández-Ramírez, Luis M. & Braga, Arthur P.S., 2020. "Improving solar forecasting using Deep Learning and Portfolio Theory integration," Energy, Elsevier, vol. 195(C).
  4. Ferlito, S. & Adinolfi, G. & Graditi, G., 2017. "Comparative analysis of data-driven methods online and offline trained to the forecasting of grid-connected photovoltaic plant production," Applied Energy, Elsevier, vol. 205(C), pages 116-129.
  5. Liu, Guanjun & Qin, Hui & Shen, Qin & Lyv, Hao & Qu, Yuhua & Fu, Jialong & Liu, Yongqi & Zhou, Jianzhong, 2021. "Probabilistic spatiotemporal solar irradiation forecasting using deep ensembles convolutional shared weight long short-term memory network," Applied Energy, Elsevier, vol. 300(C).
  6. Xwégnon Ghislain Agoua & Robin Girard & Georges Kariniotakis, 2021. "Photovoltaic Power Forecasting: Assessment of the Impact of Multiple Sources of Spatio-Temporal Data on Forecast Accuracy," Energies, MDPI, vol. 14(5), pages 1-15, March.
  7. 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).
  8. Pierro, Marco & Gentili, Damiano & Liolli, Fabio Romano & Cornaro, Cristina & Moser, David & Betti, Alessandro & Moschella, Michela & Collino, Elena & Ronzio, Dario & van der Meer, Dennis, 2022. "Progress in regional PV power forecasting: A sensitivity analysis on the Italian case study," Renewable Energy, Elsevier, vol. 189(C), pages 983-996.
  9. Pedro, Hugo T.C. & Coimbra, Carlos F.M. & David, Mathieu & Lauret, Philippe, 2018. "Assessment of machine learning techniques for deterministic and probabilistic intra-hour solar forecasts," Renewable Energy, Elsevier, vol. 123(C), pages 191-203.
  10. Han, Shuang & Qiao, Yan-hui & Yan, Jie & Liu, Yong-qian & Li, Li & Wang, Zheng, 2019. "Mid-to-long term wind and photovoltaic power generation prediction based on copula function and long short term memory network," Applied Energy, Elsevier, vol. 239(C), pages 181-191.
  11. Jiang, Zhanhong & Liu, Chao & Akintayo, Adedotun & Henze, Gregor P. & Sarkar, Soumik, 2017. "Energy prediction using spatiotemporal pattern networks," Applied Energy, Elsevier, vol. 206(C), pages 1022-1039.
  12. Sun, Mucun & Feng, Cong & Zhang, Jie, 2020. "Probabilistic solar power forecasting based on weather scenario generation," Applied Energy, Elsevier, vol. 266(C).
  13. Niu, Tong & Li, Jinkai & Wei, Wei & Yue, Hui, 2022. "A hybrid deep learning framework integrating feature selection and transfer learning for multi-step global horizontal irradiation forecasting," Applied Energy, Elsevier, vol. 326(C).
  14. Cervone, Guido & Clemente-Harding, Laura & Alessandrini, Stefano & Delle Monache, Luca, 2017. "Short-term photovoltaic power forecasting using Artificial Neural Networks and an Analog Ensemble," Renewable Energy, Elsevier, vol. 108(C), pages 274-286.
  15. Oliver Doelle & Nico Klinkenberg & Arvid Amthor & Christoph Ament, 2023. "Probabilistic Intraday PV Power Forecast Using Ensembles of Deep Gaussian Mixture Density Networks," Energies, MDPI, vol. 16(2), pages 1-17, January.
  16. Leerbeck, Kenneth & Bacher, Peder & Junker, Rune Grønborg & Goranović, Goran & Corradi, Olivier & Ebrahimy, Razgar & Tveit, Anna & Madsen, Henrik, 2020. "Short-term forecasting of CO2 emission intensity in power grids by machine learning," Applied Energy, Elsevier, vol. 277(C).
  17. Takahiro Takamatsu & Hideaki Ohtake & Takashi Oozeki & Tosiyuki Nakaegawa & Yuki Honda & Masahiro Kazumori, 2021. "Regional Solar Irradiance Forecast for Kanto Region by Support Vector Regression Using Forecast of Meso-Ensemble Prediction System," Energies, MDPI, vol. 14(11), pages 1-18, June.
  18. 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.
  19. John Boland & Sleiman Farah, 2021. "Probabilistic Forecasting of Wind and Solar Farm Output," Energies, MDPI, vol. 14(16), pages 1-15, August.
  20. Yan, Jie & Möhrlen, Corinna & Göçmen, Tuhfe & Kelly, Mark & Wessel, Arne & Giebel, Gregor, 2022. "Uncovering wind power forecasting uncertainty sources and their propagation through the whole modelling chain," Renewable and Sustainable Energy Reviews, Elsevier, vol. 165(C).
  21. Dahl, Magnus & Brun, Adam & Andresen, Gorm B., 2017. "Using ensemble weather predictions in district heating operation and load forecasting," Applied Energy, Elsevier, vol. 193(C), pages 455-465.
  22. Hong Wang & Guangyu Long & Jianxing Liao & Yan Xu & Yan Lv, 2022. "A new hybrid method for establishing point forecasting, interval forecasting, and probabilistic forecasting of landslide displacement," 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. 111(2), pages 1479-1505, March.
  23. 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.
  24. Kelachukwu J. Iheanetu, 2022. "Solar Photovoltaic Power Forecasting: A Review," Sustainability, MDPI, vol. 14(24), pages 1-31, December.
  25. Le Gal La Salle, Josselin & Badosa, Jordi & David, Mathieu & Pinson, Pierre & Lauret, Philippe, 2020. "Added-value of ensemble prediction system on the quality of solar irradiance probabilistic forecasts," Renewable Energy, Elsevier, vol. 162(C), pages 1321-1339.
  26. Luis Mazorra-Aguiar & Philippe Lauret & Mathieu David & Albert Oliver & Gustavo Montero, 2021. "Comparison of Two Solar Probabilistic Forecasting Methodologies for Microgrids Energy Efficiency," Energies, MDPI, vol. 14(6), pages 1-26, March.
  27. Liu, Yongqi & Qin, Hui & Zhang, Zhendong & Pei, Shaoqian & Wang, Chao & Yu, Xiang & Jiang, Zhiqiang & Zhou, Jianzhong, 2019. "Ensemble spatiotemporal forecasting of solar irradiation using variational Bayesian convolutional gate recurrent unit network," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
  28. Alonso-Suárez, R. & David, M. & Branco, V. & Lauret, P., 2020. "Intra-day solar probabilistic forecasts including local short-term variability and satellite information," Renewable Energy, Elsevier, vol. 158(C), pages 554-573.
  29. Qiang Ni & Shengxian Zhuang & Hanmin Sheng & Song Wang & Jian Xiao, 2017. "An Optimized Prediction Intervals Approach for Short Term PV Power Forecasting," Energies, MDPI, vol. 10(10), pages 1-16, October.
  30. Stefano Alessandrini & Tyler McCandless, 2020. "The Schaake Shuffle Technique to Combine Solar and Wind Power Probabilistic Forecasting," Energies, MDPI, vol. 13(10), pages 1-18, May.
  31. Li, Qian & Wu, Zhou & Xia, Xiaohua, 2018. "Estimate and characterize PV power at demand-side hybrid system," Applied Energy, Elsevier, vol. 218(C), pages 66-77.
  32. Thorey, J. & Chaussin, C. & Mallet, V., 2018. "Ensemble forecast of photovoltaic power with online CRPS learning," International Journal of Forecasting, Elsevier, vol. 34(4), pages 762-773.
  33. Yang, Dazhi & Yagli, Gokhan Mert & Srinivasan, Dipti, 2022. "Sub-minute probabilistic solar forecasting for real-time stochastic simulations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 153(C).
  34. Bartlett, Stuart & Dujardin, Jérôme & Kahl, Annelen & Kruyt, Bert & Manso, Pedro & Lehning, Michael, 2018. "Charting the course: A possible route to a fully renewable Swiss power system," Energy, Elsevier, vol. 163(C), pages 942-955.
  35. Di Somma, M. & Graditi, G. & Heydarian-Forushani, E. & Shafie-khah, M. & Siano, P., 2018. "Stochastic optimal scheduling of distributed energy resources with renewables considering economic and environmental aspects," Renewable Energy, Elsevier, vol. 116(PA), pages 272-287.
  36. Han, Xiaojuan & Liu, Dahe & Liu, Jian & Kong, Lingda, 2017. "Sensitivity analysis of acquisition granularity of photovoltaic output power to capacity configuration of energy storage systems," Applied Energy, Elsevier, vol. 203(C), pages 794-807.
  37. Phathutshedzo Mpfumali & Caston Sigauke & Alphonce Bere & Sophie Mulaudzi, 2019. "Day Ahead Hourly Global Horizontal Irradiance Forecasting—Application to South African Data," Energies, MDPI, vol. 12(18), pages 1-28, September.
  38. Gu, Bo & Shen, Huiqiang & Lei, Xiaohui & Hu, Hao & Liu, Xinyu, 2021. "Forecasting and uncertainty analysis of day-ahead photovoltaic power using a novel forecasting method," Applied Energy, Elsevier, vol. 299(C).
  39. Adnan, Nadia & Md Nordin, Shahrina & bin Bahruddin, Mohamad Ariff & Ali, Murad, 2018. "How trust can drive forward the user acceptance to the technology? In-vehicle technology for autonomous vehicle," Transportation Research Part A: Policy and Practice, Elsevier, vol. 118(C), pages 819-836.
  40. Fatemi, Seyyed A. & Kuh, Anthony & Fripp, Matthias, 2018. "Parametric methods for probabilistic forecasting of solar irradiance," Renewable Energy, Elsevier, vol. 129(PA), pages 666-676.
  41. Chu, Yinghao & Coimbra, Carlos F.M., 2017. "Short-term probabilistic forecasts for Direct Normal Irradiance," Renewable Energy, Elsevier, vol. 101(C), pages 526-536.
  42. Yang, Dazhi & van der Meer, Dennis, 2021. "Post-processing in solar forecasting: Ten overarching thinking tools," Renewable and Sustainable Energy Reviews, Elsevier, vol. 140(C).
  43. Demirhan, Haydar & Renwick, Zoe, 2018. "Missing value imputation for short to mid-term horizontal solar irradiance data," Applied Energy, Elsevier, vol. 225(C), pages 998-1012.
  44. 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.
  45. Yang, Dazhi & Wu, Elynn & Kleissl, Jan, 2019. "Operational solar forecasting for the real-time market," International Journal of Forecasting, Elsevier, vol. 35(4), pages 1499-1519.
  46. Pappa, Areti & Theodoropoulos, Ioannis & Galmarini, Stefano & Kioutsioukis, Ioannis, 2023. "Analog versus multi-model ensemble forecasting: A comparison for renewable energy resources," Renewable Energy, Elsevier, vol. 205(C), pages 563-573.
  47. Assouline, Dan & Mohajeri, Nahid & Scartezzini, Jean-Louis, 2018. "Large-scale rooftop solar photovoltaic technical potential estimation using Random Forests," Applied Energy, Elsevier, vol. 217(C), pages 189-211.
  48. Schinke-Nendza, A. & von Loeper, F. & Osinski, P. & Schaumann, P. & Schmidt, V. & Weber, C., 2021. "Probabilistic forecasting of photovoltaic power supply — A hybrid approach using D-vine copulas to model spatial dependencies," Applied Energy, Elsevier, vol. 304(C).
  49. Fausto Calderon-Obaldia & Jordi Badosa & Anne Migan-Dubois & Vincent Bourdin, 2020. "A Two-Step Energy Management Method Guided by Day-Ahead Quantile Solar Forecasts: Cross-Impacts on Four Services for Smart-Buildings," Energies, MDPI, vol. 13(22), pages 1-29, November.
  50. Jingyue Wang & Zheng Qian & Jingyi Wang & Yan Pei, 2020. "Hour-Ahead Photovoltaic Power Forecasting Using an Analog Plus Neural Network Ensemble Method," Energies, MDPI, vol. 13(12), pages 1-17, June.
  51. Muhammad Aslam & Jae-Myeong Lee & Hyung-Seung Kim & Seung-Jae Lee & Sugwon Hong, 2019. "Deep Learning Models for Long-Term Solar Radiation Forecasting Considering Microgrid Installation: A Comparative Study," Energies, MDPI, vol. 13(1), pages 1-15, December.
  52. Sue Ellen Haupt & Tyler C. McCandless & Susan Dettling & Stefano Alessandrini & Jared A. Lee & Seth Linden & William Petzke & Thomas Brummet & Nhi Nguyen & Branko Kosović & Gerry Wiener & Tahani Hussa, 2020. "Combining Artificial Intelligence with Physics-Based Methods for Probabilistic Renewable Energy Forecasting," Energies, MDPI, vol. 13(8), pages 1-23, April.
  53. Golestaneh, Faranak & Gooi, Hoay Beng & Pinson, Pierre, 2016. "Generation and evaluation of space–time trajectories of photovoltaic power," Applied Energy, Elsevier, vol. 176(C), pages 80-91.
  54. Zang, Haixiang & Liu, Ling & Sun, Li & Cheng, Lilin & Wei, Zhinong & Sun, Guoqiang, 2020. "Short-term global horizontal irradiance forecasting based on a hybrid CNN-LSTM model with spatiotemporal correlations," Renewable Energy, Elsevier, vol. 160(C), pages 26-41.
  55. van der Meer, D.W. & Widén, J. & Munkhammar, J., 2018. "Review on probabilistic forecasting of photovoltaic power production and electricity consumption," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 1484-1512.
  56. Salcedo-Sanz, S. & García-Herrera, R. & Camacho-Gómez, C. & Aybar-Ruíz, A. & Alexandre, E., 2018. "Wind power field reconstruction from a reduced set of representative measuring points," Applied Energy, Elsevier, vol. 228(C), pages 1111-1121.
  57. Luca Massidda & Marino Marrocu, 2018. "Quantile Regression Post-Processing of Weather Forecast for Short-Term Solar Power Probabilistic Forecasting," Energies, MDPI, vol. 11(7), pages 1-20, July.
  58. Philippe Lauret & Mathieu David & Hugo T. C. Pedro, 2017. "Probabilistic Solar Forecasting Using Quantile Regression Models," Energies, MDPI, vol. 10(10), pages 1-17, October.
  59. Kabir, M.N. & Mishra, Y. & Bansal, R.C., 2016. "Probabilistic load flow for distribution systems with uncertain PV generation," Applied Energy, Elsevier, vol. 163(C), pages 343-351.
  60. 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.
  61. Mitrentsis, Georgios & Lens, Hendrik, 2022. "An interpretable probabilistic model for short-term solar power forecasting using natural gradient boosting," Applied Energy, Elsevier, vol. 309(C).
  62. Zhang, Zhendong & Ye, Lei & Qin, Hui & Liu, Yongqi & Wang, Chao & Yu, Xiang & Yin, Xingli & Li, Jie, 2019. "Wind speed prediction method using Shared Weight Long Short-Term Memory Network and Gaussian Process Regression," Applied Energy, Elsevier, vol. 247(C), pages 270-284.
  63. Pierro, Marco & De Felice, Matteo & Maggioni, Enrico & Moser, David & Perotto, Alessandro & Spada, Francesco & Cornaro, Cristina, 2020. "Residual load probabilistic forecast for reserve assessment: A real case study," Renewable Energy, Elsevier, vol. 149(C), pages 508-522.
  64. Mayer, Martin János, 2022. "Impact of the tilt angle, inverter sizing factor and row spacing on the photovoltaic power forecast accuracy," Applied Energy, Elsevier, vol. 323(C).
  65. Bai, Yulong & Liu, Ming-De & Ding, Lin & Ma, Yong-Jie, 2021. "Double-layer staged training echo-state networks for wind speed prediction using variational mode decomposition," Applied Energy, Elsevier, vol. 301(C).
  66. Ferruzzi, Gabriella & Cervone, Guido & Delle Monache, Luca & Graditi, Giorgio & Jacobone, Francesca, 2016. "Optimal bidding in a Day-Ahead energy market for Micro Grid under uncertainty in renewable energy production," Energy, Elsevier, vol. 106(C), pages 194-202.
  67. Shahriari, M. & Cervone, G. & Clemente-Harding, L. & Delle Monache, L., 2020. "Using the analog ensemble method as a proxy measurement for wind power predictability," Renewable Energy, Elsevier, vol. 146(C), pages 789-801.
  68. Carneiro, Tatiane C. & Rocha, Paulo A.C. & Carvalho, Paulo C.M. & Fernández-Ramírez, Luis M., 2022. "Ridge regression ensemble of machine learning models applied to solar and wind forecasting in Brazil and Spain," Applied Energy, Elsevier, vol. 314(C).
  69. Chen, Xiang & Ding, Kun & Yang, Hang & Chen, Xihui & Zhang, Jingwei & Jiang, Meng & Gao, Ruiguang & Liu, Zengquan, 2023. "Research on real-time identification method of model parameters for the photovoltaic array," Applied Energy, Elsevier, vol. 342(C).
  70. Munkhammar, Joakim & van der Meer, Dennis & Widén, Joakim, 2021. "Very short term load forecasting of residential electricity consumption using the Markov-chain mixture distribution (MCM) model," Applied Energy, Elsevier, vol. 282(PA).
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.