Crossref journal-article
Elsevier BV
Energy Conversion and Management (78)
Bibliography

De Giorgi, M. G., Congedo, P. M., Malvoni, M., & Laforgia, D. (2015). Error analysis of hybrid photovoltaic power forecasting models: A case study of mediterranean climate. Energy Conversion and Management, 100, 117–130.

Authors 4
  1. Maria Grazia De Giorgi (first)
  2. Paolo Maria Congedo (additional)
  3. Maria Malvoni (additional)
  4. Domenico Laforgia (additional)
References 65 Referenced 94
  1. 10.1016/j.enconman.2012.12.017 / Energy Convers Manage / Performance measurements of monocrystalline silicon PV modules in South-eastern Italy by Congedo (2013)
  2. De Giorgi MG, Congedo PM, Malvoni M, Tarantino M. Short-term power forecasting by statistical methods for photovoltaic plants in south Italy. In: 4th IMEKO TC19 symposium on environmental instrumentation and measurements: protection environment, climate changes and pollution control, June 3–4, Lecce, Italy; 2013. p 171–5.
  3. 10.1016/j.enconman.2009.10.034 / Energy Convers Manage / An adaptive model for predicting of global, direct and diffuse hourly solar irradiance by Mellit (2010)
  4. 10.1016/j.solener.2010.02.006 / Sol Energy / A 24-h forecast of solar irradiance using artificial neural network: application for performance prediction of a grid-connected PV plant at Trieste, Italy by Mellit (2010)
  5. 10.1016/j.apenergy.2011.12.052 / Appl Energy / Real-time prediction models for output power and efficiency of grid-connected solar photovoltaic systems by Su (2012)
  6. 10.1016/j.rser.2008.02.002 / Renew Sustain Energy Rev / A review on the forecasting of wind speed and generated power by Lei (2009)
  7. 10.1016/S0951-8339(02)00053-9 / Mar Struct / Forecasting wind with neural networks by More (2003)
  8. 10.1016/j.enconman.2005.10.010 / Energy Convers Manage / Forecasting of electricity prices with neural networks by Gareta (2006)
  9. 10.1016/j.enconman.2013.11.019 / Energy Convers Manage / Novel effects of demand side management data on accuracy of electrical energy consumption modeling and long-term forecasting by Ardakani (2014)
  10. Nagi J, Yap KS, Tiong SK, Ahmend SK. Electrical power load forecasting using hybrid self-organizing maps and support data machine. In: Proc. of the 2nd international power engineering and optimization conference (PEOCO), June 4–5, Shah Alam, Malaysia; 2008. p. 51–56.
  11. De Giorgi MG, Ficarella A, Russo MG. Short-term wind forecasting using artificial neural networks (ANNs). In: Second international conference on energy and sustainability, June 23–25, Bologna, Italy; 2009. (10.2495/ESU090181)
  12. 10.1016/j.enconman.2014.02.017 / Energy Convers Manage / Wind speed estimation using multilayer perceptron by Velo (2014)
  13. 10.1016/j.renene.2008.06.010 / Renewable Energy / Characterisation of Si-crystalline PV modules by artificial neural networks by Almonacid (2009)
  14. 10.1016/j.renene.2009.05.020 / Renewable Energy / Estimation of the energy of a PV generator using artificial neural network by Almonacid (2009)
  15. 10.1016/j.energy.2010.10.028 / Energy / Calculation of the energy provided by a PV generator. Comparative study: Conventional methods vs. Artificial neural networks by Almonacid (2011)
  16. 10.1016/S0038-092X(98)00032-2 / Sol Energy / Short-term forecasting of wind speed and related electrical power by Alexiadis (1998)
  17. 10.1016/j.apenergy.2009.12.013 / Appl Energy / On comparing three artificial neural networks for wind speed forecasting by Li (2010)
  18. 10.1016/j.ejor.2012.02.042 / Eur J Oper Res / Forecasting wind speed with recurrent neural networks by Cao (2012)
  19. 10.1016/S0960-1481(99)00125-1 / Renewable Energy / A comparison of various forecasting techniques applied to mean hourly wind speed time series by Sfetsos (2000)
  20. 10.1016/j.rser.2007.01.015 / Renew Sust Energy Rev / A review on the young history of the wind power short-term prediction by Costa (2008)
  21. 10.1016/j.enconman.2010.07.053 / Energy Convers Manage / Very short-term wind speed prediction: a new artificial neural network-Markov chain model by Pourmousavi Kani (2011)
  22. 10.1016/j.apenergy.2011.12.085 / Appl Energy / A radial basis function neural network based approach for the electrical characteristics estimation of a photovoltaic module by Bonanno (2012)
  23. 10.1016/j.enconman.2013.11.006 / Energy Convers Manage / Classification of methods for annual energy harvesting calculations of photovoltaic generators by Rus-Casas (2014)
  24. 10.1016/j.apenergy.2009.09.005 / Appl Energy / The application of artificial neural networks to mapping of wind speed profile for energy application in Nigeria by Fadare (2010)
  25. 10.1016/j.apenergy.2008.12.005 / Appl Energy / Modelling of solar energy potential in Nigeria using an artificial neural network model by Fadare (2009)
  26. 10.1109/60.556376 / IEEE Trans Energy Conver / Wind power forecasting using advanced neural networks models by Kariniotakis (1996)
  27. 10.1016/j.apenergy.2010.10.035 / Appl Energy / Error analysis of short term wind power prediction models by De Giorgi (2011)
  28. Yona A, Senjyu T, Funabashi T. Application of recurrent neural network to short-term-ahead generating power forecasting for photovoltaic system. In: Power Engineering Society General Meeting, 24–28 June, Tampa, FL; 2007. p. 1–6. (10.1109/ISAP.2007.4441657)
  29. 10.1016/S0306-2619(02)00016-8 / Appl Energy / Solar radiation estimation using artificial neural networks by Dorvlo (2002)
  30. 10.1016/j.enconman.2013.07.003 / Energy Convers Manage / Forecasting hourly global solar radiation using hybrid k-means and nonlinear autoregressive neural network models by Benmouiza (2013)
  31. 10.1016/j.enconman.2012.11.010 / Energy Conver Manage / Neural network based method for conversion of solar radiation data by Celik (2013)
  32. 10.1016/j.egypro.2011.09.024 / Energy Proc / Forecasting power output of PV grid connected system in Thailand without using solar radiation measurement by Chupong (2011)
  33. {'key': '10.1016/j.enconman.2015.04.078_b0165', 'series-title': 'The nature of statistical learning theory', 'isbn-type': 'print', 'author': 'Vapnik', 'year': '1995', 'ISBN': 'https://id.crossref.org/isbn/9780387987804'} / The nature of statistical learning theory by Vapnik (1995)
  34. {'year': '1998', 'series-title': 'Statistical learning theory (adaptive and learning systems for signal processing, communications and control series)', 'author': 'Vapnik', 'key': '10.1016/j.enconman.2015.04.078_b0170'} / Statistical learning theory (adaptive and learning systems for signal processing, communications and control series) by Vapnik (1998)
  35. 10.1016/j.enconman.2005.06.013 / Energy Convers Manage / Support vector machine based battery model for electric vehicles by Junping (2006)
  36. 10.1016/j.enconman.2005.02.004 / Energy Convers Manage / Support vector machines with simulated annealing algorithms in electricity load forecasting by Pai (2005)
  37. {'issue': 'August', 'key': '10.1016/j.enconman.2015.04.078_b0185', 'first-page': '162', 'article-title': 'Performance evaluation of short term wind speed prediction techniques', 'volume': '8', 'author': 'Sreelakshmi', 'year': '2008', 'journal-title': 'IJCSNS Int J Comput Sci Network Secur'} / IJCSNS Int J Comput Sci Network Secur / Performance evaluation of short term wind speed prediction techniques by Sreelakshmi (2008)
  38. 10.1016/j.renene.2003.11.009 / Renewable Energy / Support vector machines for wind speed prediction by Mohandes (2004)
  39. 10.1016/j.enconman.2013.06.034 / Energy Convers Manage / Assessing the potential of support vector machine for estimating daily solar radiation using sunshine duration by Chen (2013)
  40. 10.1016/j.renene.2010.06.024 / Renewable Energy / Estimation of monthly solar radiation from measured temperature using support vector machines e a case study by Chen (2011)
  41. Fonseca JGS, Oozeki T, Takashima T, Koshimizu G, Uchida Y, Ogimoto K. Photovoltaic power production forecasts with support vector regression: a study on the forecast horizon. In: 37th IEEE photovoltaic specialists conference, 19–24 June 2011. p. 2579–83.
  42. 10.1016/j.enconman.2008.01.008 / Energy Convers Manage / Machine learning based switching model for electricity load forecasting by Fan (2008)
  43. 10.1109/TPWRS.2005.860944 / IEEE Trans Power Syst / Short-term load forecasting based on an adaptive hybrid method by Fan (2006)
  44. {'year': '2002', 'series-title': 'Least squares support vector machines', 'author': 'Suykens', 'key': '10.1016/j.enconman.2015.04.078_b0220'} / Least squares support vector machines by Suykens (2002)
  45. 10.1007/978-3-642-22194-1_56 / Intell Decis Technol Smart Innov Syst Technol / Predicting of the short term wind speed by using a real valued genetic algorithm based least squared support data machine by Huang (2011)
  46. 10.1016/j.enconman.2010.11.007 / Energy Convers Manage / Fine tuning support vector machines for short-term wind speed forecasting by Zhou (2011)
  47. 10.3390/en7085251 / Energies / Comparison between wind power prediction models based on wavelet decomposition with least-squares support vector machine (LS-SVM) and artificial neural network (ANN) by De Giorgi (2014)
  48. 10.1109/34.192463 / IEEE Trans Pattern Anal Mach Intell / A theory for multiresolution signal decomposition: the wavelet representation by Mallat (1989)
  49. 10.1016/j.energy.2011.05.006 / Energy / Assessment of the benefits of numerical weather predictions in wind power forecasting based on statistical methods by De Giorgi (2011)
  50. 10.1016/j.enconman.2013.07.013 / Energy Convers Manage / Day-ahead electricity prices forecasting by a modified CGSA technique and hybrid WT in LSSVM based scheme by Shayeghi (2013)
  51. 10.1016/j.energy.2008.09.020 / Energy / Short-term load forecasting of power systems by combination of wavelet transform and neuro-evolutionary algorithm by Amjady (2009)
  52. 10.1016/j.enconman.2014.03.022 / Energy Convers Manage / Expert energy management of a micro-grid considering wind energy uncertainty by Motevasel (2014)
  53. Italian Authority for the Electricity Gas and Water, Resolution 522/14/R/eel. Disposizioni in materia di dispacciamento delle fonti rinnovabili non programmabili a seguito della sentenza del Consiglio di Stato. 2014. Sezione Sesta-9 giugno 2014, n. 2936.
  54. 10.1016/j.renene.2015.01.047 / Renewable Energy / Energy storage for PV power plant dispatching by Delfanti (2015)
  55. De Giorgi MG, Congedo PM, Malvoni M. Photovoltaic power forecasting using statistical methods: impact of weather data. IET Science, Measurement and Technology. p. 1–8. doi: http://dx.doi.org/10.1049/iet-smt.2013.0135. (10.1049/iet-smt.2013.0135)
  56. http://supervisione.espe.it/fotovoltaicoWeb/index.htm.
  57. {'key': '10.1016/j.enconman.2015.04.078_b0285', 'first-page': '475', 'article-title': 'Standardizing the performance evaluation of short-term wind power prediction models', 'volume': '29', 'author': 'Madsen', 'year': '2005', 'journal-title': 'Wind Energy'} / Wind Energy / Standardizing the performance evaluation of short-term wind power prediction models by Madsen (2005)
  58. {'year': '2013', 'series-title': 'Solar energy forecasting and resource assessment', 'author': 'Carlos', 'key': '10.1016/j.enconman.2015.04.078_b0290'} / Solar energy forecasting and resource assessment by Carlos (2013)
  59. {'issue': '2', 'key': '10.1016/j.enconman.2015.04.078_b0295', 'first-page': '177', 'article-title': 'On the uncertainty of wind power predictions – analysis of the forecast accuracy and statistical distribution of errors', 'volume': '127', 'author': 'Lange', 'year': '2005', 'journal-title': 'J SolEnergy Eng'} / J SolEnergy Eng / On the uncertainty of wind power predictions – analysis of the forecast accuracy and statistical distribution of errors by Lange (2005)
  60. 10.1016/j.jhydrol.2003.12.033 / J Hydrol / Comparison of static-feedforward and dynamic-feedback neural networks for rainfall–runoff modeling by Chiang (2004)
  61. {'key': '10.1016/j.enconman.2015.04.078_b0305', 'first-page': '593', 'volume': 'vol. 1', 'author': 'Hecht-Nielsen', 'year': '1989'} by Hecht-Nielsen (1989)
  62. Duffie JA, Beckman WA. Solar engineering of thermal processes. 4th ed; 2013. doi: http://dx.doi.org/10.1002/9781118671603. (10.1002/9781118671603)
  63. http://www.solaritaly.enea.it/StrDiagrammiSolari/X12Mesi1.php.
  64. https://www.mercatoelettrico.org/en/tools/Glossario.aspx.
  65. 10.1016/j.enconman.2014.01.052 / Energy Convers Manage / Estimation of demand response to energy price signals in energy consumption behaviour in Beijing, China by He (2014)
Dates
Type When
Created 10 years, 3 months ago (May 15, 2015, 10:45 a.m.)
Deposited 3 years, 9 months ago (Oct. 30, 2021, 7:50 p.m.)
Indexed 2 weeks, 6 days ago (Aug. 7, 2025, 4:59 p.m.)
Issued 10 years ago (Aug. 1, 2015)
Published 10 years ago (Aug. 1, 2015)
Published Print 10 years ago (Aug. 1, 2015)
Funders 0

None

@article{De_Giorgi_2015, title={Error analysis of hybrid photovoltaic power forecasting models: A case study of mediterranean climate}, volume={100}, ISSN={0196-8904}, url={http://dx.doi.org/10.1016/j.enconman.2015.04.078}, DOI={10.1016/j.enconman.2015.04.078}, journal={Energy Conversion and Management}, publisher={Elsevier BV}, author={De Giorgi, Maria Grazia and Congedo, Paolo Maria and Malvoni, Maria and Laforgia, Domenico}, year={2015}, month=aug, pages={117–130} }