IDEAS home Printed from https://ideas.repec.org/r/eee/renene/v35y2010i2p478-483.html
   My bibliography  Save this item

Modelling of diffuse solar fraction with multiple predictors

Citations

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


Cited by:

  1. Mayer, Martin János, 2022. "Benefits of physical and machine learning hybridization for photovoltaic power forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
  2. Lou, Siwei & Huang, Yu & Li, Danny H.W. & Xia, Dawei & Zhou, Xiaoqing & Zhao, Yang, 2020. "A novel method for fast sky conditions identification from global solar radiation measurements," Renewable Energy, Elsevier, vol. 161(C), pages 77-90.
  3. Boland, John & Huang, Jing & Ridley, Barbara, 2013. "Decomposing global solar radiation into its direct and diffuse components," Renewable and Sustainable Energy Reviews, Elsevier, vol. 28(C), pages 749-756.
  4. Liu, Yujun & Yao, Ling & Jiang, Hou & Lu, Ning & Qin, Jun & Liu, Tang & Zhou, Chenghu, 2022. "Spatial estimation of the optimum PV tilt angles in China by incorporating ground with satellite data," Renewable Energy, Elsevier, vol. 189(C), pages 1249-1258.
  5. Oh, Myeongchan & Kim, Chang Ki & Kim, Boyoung & Yun, Changyeol & Kim, Jin-Young & Kang, Yongheack & Kim, Hyun-Goo, 2022. "Analysis of minute-scale variability for enhanced separation of direct and diffuse solar irradiance components using machine learning algorithms," Energy, Elsevier, vol. 241(C).
  6. Starke, Allan R. & Lemos, Leonardo F.L. & Boland, John & Cardemil, José M. & Colle, Sergio, 2018. "Resolution of the cloud enhancement problem for one-minute diffuse radiation prediction," Renewable Energy, Elsevier, vol. 125(C), pages 472-484.
  7. Bianchi, Emilio & Guozden, Tomás & Kozulj, Roberto, 2022. "Assessing low frequency variations in solar and wind power and their climatic teleconnections," Renewable Energy, Elsevier, vol. 190(C), pages 560-571.
  8. Sharifzadeh, Mahdi & Sikinioti-Lock, Alexandra & Shah, Nilay, 2019. "Machine-learning methods for integrated renewable power generation: A comparative study of artificial neural networks, support vector regression, and Gaussian Process Regression," Renewable and Sustainable Energy Reviews, Elsevier, vol. 108(C), pages 513-538.
  9. Weatherford, Vergil C. & (John) Zhai, Zhiqiang, 2015. "Affordable solar-assisted biogas digesters for cold climates: Experiment, model, verification and analysis," Applied Energy, Elsevier, vol. 146(C), pages 209-216.
  10. Diane Palmer & Elena Koubli & Tom Betts & Ralph Gottschalg, 2017. "The UK Solar Farm Fleet: A Challenge for the National Grid? †," Energies, MDPI, vol. 10(8), pages 1-22, August.
  11. Pfenninger, Stefan, 2017. "Dealing with multiple decades of hourly wind and PV time series in energy models: A comparison of methods to reduce time resolution and the planning implications of inter-annual variability," Applied Energy, Elsevier, vol. 197(C), pages 1-13.
  12. Deo, Ravinesh C. & Wen, Xiaohu & Qi, Feng, 2016. "A wavelet-coupled support vector machine model for forecasting global incident solar radiation using limited meteorological dataset," Applied Energy, Elsevier, vol. 168(C), pages 568-593.
  13. Geoffrey J. Blanford & Christoph Weissbart, 2019. "A Framework for Modeling the Dynamics of Power Markets – The EU-REGEN Model," ifo Working Paper Series 307, ifo Institute - Leibniz Institute for Economic Research at the University of Munich.
  14. Copper, J.K. & Sproul, A.B., 2012. "Comparative study of mathematical models in estimating solar irradiance for Australia," Renewable Energy, Elsevier, vol. 43(C), pages 130-139.
  15. Qin, Jun & Jiang, Hou & Lu, Ning & Yao, Ling & Zhou, Chenghu, 2022. "Enhancing solar PV output forecast by integrating ground and satellite observations with deep learning," Renewable and Sustainable Energy Reviews, Elsevier, vol. 167(C).
  16. Richardson, David B. & Harvey, L.D.D., 2015. "Strategies for correlating solar PV array production with electricity demand," Renewable Energy, Elsevier, vol. 76(C), pages 432-440.
  17. Mayer, Martin János & Yang, Dazhi, 2022. "Probabilistic photovoltaic power forecasting using a calibrated ensemble of model chains," Renewable and Sustainable Energy Reviews, Elsevier, vol. 168(C).
  18. Bertrand, Cédric & Vanderveken, Gilles & Journée, Michel, 2015. "Evaluation of decomposition models of various complexity to estimate the direct solar irradiance over Belgium," Renewable Energy, Elsevier, vol. 74(C), pages 618-626.
  19. Marques Filho, Edson P. & Oliveira, Amauri P. & Vita, Willian A. & Mesquita, Francisco L.L. & Codato, Georgia & Escobedo, João F. & Cassol, Mariana & França, José Ricardo A., 2016. "Global, diffuse and direct solar radiation at the surface in the city of Rio de Janeiro: Observational characterization and empirical modeling," Renewable Energy, Elsevier, vol. 91(C), pages 64-74.
  20. Orehounig, Kristina & Dervishi, Sokol & Mahdavi, Ardeshir, 2014. "Computational derivation of irradiance on building surfaces: An empirically-based model comparison," Renewable Energy, Elsevier, vol. 71(C), pages 185-192.
  21. 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).
  22. Chinchilla, Monica & Santos-Martín, David & Carpintero-Rentería, Miguel & Lemon, Scott, 2021. "Worldwide annual optimum tilt angle model for solar collectors and photovoltaic systems in the absence of site meteorological data," Applied Energy, Elsevier, vol. 281(C).
  23. Yu Wang & Mikael Boulic & Robyn Phipps & Manfred Plagmann & Chris Cunningham, 2020. "Experimental Performance of a Solar Air Collector with a Perforated Back Plate in New Zealand," Energies, MDPI, vol. 13(6), pages 1-16, March.
  24. Mayer, Martin János & Yang, Dazhi, 2023. "Pairing ensemble numerical weather prediction with ensemble physical model chain for probabilistic photovoltaic power forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 175(C).
  25. Diane Palmer & Ian Cole & Tom Betts & Ralph Gottschalg, 2017. "Interpolating and Estimating Horizontal Diffuse Solar Irradiation to Provide UK-Wide Coverage: Selection of the Best Performing Models," Energies, MDPI, vol. 10(2), pages 1-23, February.
  26. Abreu, Edgar F.M. & Canhoto, Paulo & Prior, Victor & Melicio, R., 2018. "Solar resource assessment through long-term statistical analysis and typical data generation with different time resolutions using GHI measurements," Renewable Energy, Elsevier, vol. 127(C), pages 398-411.
  27. Lou, Siwei & Li, Danny H.W. & Lam, Joseph C., 2017. "CIE Standard Sky classification by accessible climatic indices," Renewable Energy, Elsevier, vol. 113(C), pages 347-356.
  28. Abhnil Amtesh Prasad & Merlinde Kay, 2020. "Assessment of Simulated Solar Irradiance on Days of High Intermittency Using WRF-Solar," Energies, MDPI, vol. 13(2), pages 1-22, January.
  29. Liu, Peirong & Tong, Xiaojuan & Zhang, Jinsong & Meng, Ping & Li, Jun & Zhang, Jingru, 2020. "Estimation of half-hourly diffuse solar radiation over a mixed plantation in north China," Renewable Energy, Elsevier, vol. 149(C), pages 1360-1369.
  30. Martin Hofmann & Gunther Seckmeyer, 2017. "A New Model for Estimating the Diffuse Fraction of Solar Irradiance for Photovoltaic System Simulations," Energies, MDPI, vol. 10(2), pages 1-21, February.
  31. Müller, Johannes & Folini, Doris & Wild, Martin & Pfenninger, Stefan, 2019. "CMIP-5 models project photovoltaics are a no-regrets investment in Europe irrespective of climate change," Energy, Elsevier, vol. 171(C), pages 135-148.
  32. Copper, J.K. & Sproul, A.B., 2013. "Comparative building simulation study utilising measured and estimated solar irradiance for Australian locations," Renewable Energy, Elsevier, vol. 53(C), pages 86-93.
  33. Haghdadi, Navid & Copper, Jessie & Bruce, Anna & MacGill, Iain, 2017. "A method to estimate the location and orientation of distributed photovoltaic systems from their generation output data," Renewable Energy, Elsevier, vol. 108(C), pages 390-400.
  34. Yang, Dazhi, 2022. "Estimating 1-min beam and diffuse irradiance from the global irradiance: A review and an extensive worldwide comparison of latest separation models at 126 stations," Renewable and Sustainable Energy Reviews, Elsevier, vol. 159(C).
  35. Torres, J.L. & De Blas, M. & García, A. & de Francisco, A., 2010. "Comparative study of various models in estimating hourly diffuse solar irradiance," Renewable Energy, Elsevier, vol. 35(6), pages 1325-1332.
  36. David, Mathieu & Lauret, Philippe & Boland, John, 2013. "Evaluating tilted plane models for solar radiation using comprehensive testing procedures, at a southern hemisphere location," Renewable Energy, Elsevier, vol. 51(C), pages 124-131.
  37. Madeleine McPherson & Theofilos Sotiropoulos-Michalakakos & LD Danny Harvey & Bryan Karney, 2017. "An Open-Access Web-Based Tool to Access Global, Hourly Wind and Solar PV Generation Time-Series Derived from the MERRA Reanalysis Dataset," Energies, MDPI, vol. 10(7), pages 1-14, July.
  38. Starke, Allan R. & Lemos, Leonardo F.L. & Barni, Cristian M. & Machado, Rubinei D. & Cardemil, José M. & Boland, John & Colle, Sergio, 2021. "Assessing one-minute diffuse fraction models based on worldwide climate features," Renewable Energy, Elsevier, vol. 177(C), pages 700-714.
  39. Prasad, Abhnil A. & Taylor, Robert A. & Kay, Merlinde, 2015. "Assessment of direct normal irradiance and cloud connections using satellite data over Australia," Applied Energy, Elsevier, vol. 143(C), pages 301-311.
  40. Mayer, Martin János & Gróf, Gyula, 2021. "Extensive comparison of physical models for photovoltaic power forecasting," Applied Energy, Elsevier, vol. 283(C).
  41. Jentsch, Mark F. & James, Patrick A.B. & Bourikas, Leonidas & Bahaj, AbuBakr S., 2013. "Transforming existing weather data for worldwide locations to enable energy and building performance simulation under future climates," Renewable Energy, Elsevier, vol. 55(C), pages 514-524.
  42. Elliston, Ben & MacGill, Iain & Prasad, Abhnil & Kay, Merlinde, 2015. "Spatio-temporal characterisation of extended low direct normal irradiance events over Australia using satellite derived solar radiation data," Renewable Energy, Elsevier, vol. 74(C), pages 633-639.
  43. Every, Jeremy P. & Li, Li & Dorrell, David G., 2020. "Köppen-Geiger climate classification adjustment of the BRL diffuse irradiation model for Australian locations," Renewable Energy, Elsevier, vol. 147(P1), pages 2453-2469.
  44. Kuo, Chia-Wei & Chang, Wen-Chey & Chang, Keh-Chin, 2014. "Modeling the hourly solar diffuse fraction in Taiwan," Renewable Energy, Elsevier, vol. 66(C), pages 56-61.
  45. Pfenninger, Stefan & Keirstead, James, 2015. "Renewables, nuclear, or fossil fuels? Scenarios for Great Britain’s power system considering costs, emissions and energy security," Applied Energy, Elsevier, vol. 152(C), pages 83-93.
  46. Lauret, Philippe & Boland, John & Ridley, Barbara, 2013. "Bayesian statistical analysis applied to solar radiation modelling," Renewable Energy, Elsevier, vol. 49(C), pages 124-127.
  47. Copper, J.K. & Sproul, A.B. & Jarnason, S., 2016. "Photovoltaic (PV) performance modelling in the absence of onsite measured plane of array irradiance (POA) and module temperature," Renewable Energy, Elsevier, vol. 86(C), pages 760-769.
  48. Rodríguez-Muñoz, J.M. & Monetta, A. & Alonso-Suárez, R. & Bove, I. & Abal, G., 2021. "Correction methods for shadow-band diffuse irradiance measurements: assessing the impact of local adaptation," Renewable Energy, Elsevier, vol. 178(C), pages 830-844.
  49. Rojas, Redlich García & Alvarado, Natalia & Boland, John & Escobar, Rodrigo & Castillejo-Cuberos, Armando, 2019. "Diffuse fraction estimation using the BRL model and relationship of predictors under Chilean, Costa Rican and Australian climatic conditions," Renewable Energy, Elsevier, vol. 136(C), pages 1091-1106.
  50. Lin, Chun-Tin & Chang, Keh-Chin & Chung, Kung-Ming, 2023. "Re-modeling the solar diffuse fraction in Taiwan on basis of a typical-meteorological-year data," Renewable Energy, Elsevier, vol. 204(C), pages 823-835.
  51. Moretón, R. & Lorenzo, E. & Pinto, A. & Muñoz, J. & Narvarte, L., 2017. "From broadband horizontal to effective in-plane irradiation: A review of modelling and derived uncertainty for PV yield prediction," Renewable and Sustainable Energy Reviews, Elsevier, vol. 78(C), pages 886-903.
  52. Huang, Kuo-Tsang, 2020. "Identifying a suitable hourly solar diffuse fraction model to generate the typical meteorological year for building energy simulation application," Renewable Energy, Elsevier, vol. 157(C), pages 1102-1115.
  53. Pfenninger, Stefan & Staffell, Iain, 2016. "Long-term patterns of European PV output using 30 years of validated hourly reanalysis and satellite data," Energy, Elsevier, vol. 114(C), pages 1251-1265.
  54. Chen, Ji-Long & He, Lei & Chen, Qiao & Lv, Ming-Quan & Zhu, Hong-Lin & Wen, Zhao-Fei & Wu, Sheng-Jun, 2019. "Study of monthly mean daily diffuse and direct beam radiation estimation with MODIS atmospheric product," Renewable Energy, Elsevier, vol. 132(C), pages 221-232.
  55. Li, Huashan & Ma, Weibin & Wang, Xianlong & Lian, Yongwang, 2011. "Estimating monthly average daily diffuse solar radiation with multiple predictors: A case study," Renewable Energy, Elsevier, vol. 36(7), pages 1944-1948.
  56. Hu, Jing & Harmsen, Robert & Crijns-Graus, Wina & Worrell, Ernst, 2019. "Geographical optimization of variable renewable energy capacity in China using modern portfolio theory," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
  57. Martin Hofmann & Gunther Seckmeyer, 2017. "Influence of Various Irradiance Models and Their Combination on Simulation Results of Photovoltaic Systems," Energies, MDPI, vol. 10(10), pages 1-24, September.
  58. Zeljković, Čedomir & Mršić, Predrag & Erceg, Bojan & Lekić, Đorđe & Kitić, Nemanja & Matić, Petar, 2022. "Optimal sizing of photovoltaic-wind-diesel-battery power supply for mobile telephony base stations," Energy, Elsevier, vol. 242(C).
  59. Deo, Ravinesh C. & Şahin, Mehmet, 2017. "Forecasting long-term global solar radiation with an ANN algorithm coupled with satellite-derived (MODIS) land surface temperature (LST) for regional locations in Queensland," Renewable and Sustainable Energy Reviews, Elsevier, vol. 72(C), pages 828-848.
  60. Pérez-Burgos, Ana & Román, Roberto & Bilbao, Julia & de Miguel, Argimiro & Oteiza, Pilar, 2015. "Reconstruction of long-term direct solar irradiance data series using a model based on the Cloud Modification Factor," Renewable Energy, Elsevier, vol. 77(C), pages 115-124.
  61. Lou, Siwei & Li, Danny H.W. & Lam, Joseph C. & Chan, Wilco W.H., 2016. "Prediction of diffuse solar irradiance using machine learning and multivariable regression," Applied Energy, Elsevier, vol. 181(C), pages 367-374.
  62. Rouhollahi, Mina & Whaley, David & Behrend, Monica & Byrne, Josh & Boland, John, 2022. "The role of residential tree arrangement: A scoping review of energy efficiency in temperate to subtropical climate zones," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
  63. John Boland, 2020. "Characterising Seasonality of Solar Radiation and Solar Farm Output," Energies, MDPI, vol. 13(2), pages 1-15, January.
  64. Hassan, Muhammed A. & Akoush, Bassem M. & Abubakr, Mohamed & Campana, Pietro Elia & Khalil, Adel, 2021. "High-resolution estimates of diffuse fraction based on dynamic definitions of sky conditions," Renewable Energy, Elsevier, vol. 169(C), pages 641-659.
  65. Das, Aparna & Paul, Saikat Kumar, 2015. "Artificial illumination during daytime in residential buildings: Factors, energy implications and future predictions," Applied Energy, Elsevier, vol. 158(C), pages 65-85.
  66. Lemos, Leonardo F.L. & Starke, Allan R. & Boland, John & Cardemil, José M. & Machado, Rubinei D. & Colle, Sergio, 2017. "Assessment of solar radiation components in Brazil using the BRL model," Renewable Energy, Elsevier, vol. 108(C), pages 569-580.
  67. Santos-Alamillos, F.J. & Pozo-Vázquez, D. & Ruiz-Arias, J.A. & Von Bremen, L. & Tovar-Pescador, J., 2015. "Combining wind farms with concentrating solar plants to provide stable renewable power," Renewable Energy, Elsevier, vol. 76(C), pages 539-550.
IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.