Littérature scientifique sur le sujet « SOLAR ENERGY FORECASTING »
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Articles de revues sur le sujet "SOLAR ENERGY FORECASTING"
Sangrody, Hossein, Morteza Sarailoo, Ning Zhou, Nhu Tran, Mahdi Motalleb et Elham Foruzan. « Weather forecasting error in solar energy forecasting ». IET Renewable Power Generation 11, no 10 (11 juillet 2017) : 1274–80. http://dx.doi.org/10.1049/iet-rpg.2016.1043.
Texte intégralChaudhary, Pankaj, Rohith Gattu, Soundarajan Ezekiel et James Allen Rodger. « Forecasting Solar Radiation ». Journal of Cases on Information Technology 23, no 4 (octobre 2021) : 1–21. http://dx.doi.org/10.4018/jcit.296263.
Texte intégralEl hendouzi, Abdelhakim, et Abdennaser Bourouhou. « Solar Photovoltaic Power Forecasting ». Journal of Electrical and Computer Engineering 2020 (31 décembre 2020) : 1–21. http://dx.doi.org/10.1155/2020/8819925.
Texte intégralA. G. M. Amarasinghe, P., N. S. Abeygunawardana, T. N. Jayasekara, E. A. J. P. Edirisinghe et S. K. Abeygunawardane. « Ensemble models for solar power forecasting—a weather classification approach ». AIMS Energy 8, no 2 (2020) : 252–71. http://dx.doi.org/10.3934/energy.2020.2.252.
Texte intégralPaulescu, Marius, Nicoleta Stefu, Ciprian Dughir, Robert Blaga, Andreea Sabadus, Eugenia Paulescu et Sorin Bojin. « Online Forecasting of the Solar Energy Production ». Annals of West University of Timisoara - Physics 60, no 1 (1 août 2018) : 104–10. http://dx.doi.org/10.2478/awutp-2018-0011.
Texte intégralNath, N. C., W. Sae-Tang et C. Pirak. « Machine Learning-Based Solar Power Energy Forecasting ». Journal of the Society of Automotive Engineers Malaysia 4, no 3 (1 septembre 2020) : 307–22. http://dx.doi.org/10.56381/jsaem.v4i3.25.
Texte intégralMadhiarasan, Manoharan, Mohamed Louzazni et Brahim Belmahdi. « Statistical Analysis of Novel Ensemble Recursive Radial Basis Function Neural Network Performance on Global Solar Irradiance Forecasting ». Journal of Electrical and Computer Engineering 2023 (28 mars 2023) : 1–10. http://dx.doi.org/10.1155/2023/2554355.
Texte intégralAl-Ali, Elham M., Yassine Hajji, Yahia Said, Manel Hleili, Amal M. Alanzi, Ali H. Laatar et Mohamed Atri. « Solar Energy Production Forecasting Based on a Hybrid CNN-LSTM-Transformer Model ». Mathematics 11, no 3 (28 janvier 2023) : 676. http://dx.doi.org/10.3390/math11030676.
Texte intégralChodakowska, Ewa, Joanicjusz Nazarko, Łukasz Nazarko, Hesham S. Rabayah, Raed M. Abendeh et Rami Alawneh. « ARIMA Models in Solar Radiation Forecasting in Different Geographic Locations ». Energies 16, no 13 (28 juin 2023) : 5029. http://dx.doi.org/10.3390/en16135029.
Texte intégralVennila, C., Anita Titus, T. Sri Sudha, U. Sreenivasulu, N. Pandu Ranga Reddy, K. Jamal, Dayadi Lakshmaiah, P. Jagadeesh et Assefa Belay. « Forecasting Solar Energy Production Using Machine Learning ». International Journal of Photoenergy 2022 (30 avril 2022) : 1–7. http://dx.doi.org/10.1155/2022/7797488.
Texte intégralThèses sur le sujet "SOLAR ENERGY FORECASTING"
Montornès, Torrecillas Alex. « A study of the shortwave schemes in the Weather Research and Forecasting model ». Doctoral thesis, Universitat de Barcelona, 2017. http://hdl.handle.net/10803/401501.
Texte intégralL’objectiu principal d’aquesta tesi ´es la identificaci´o i quantificaci´o de les fonts d’error que tenen una contribuci´o directa o indirecta en la precisi´o dels esquemes solars, particularment en aquells disponibles en el model Weather Research and Forecasting (WRF-ARW), `ampliament emprat en el sector de l’energia solar. Les fonts d’error s´on limitacions en la representaci´o del transport radiatiu com a consequ¨`encia del conjunt d’aproximacions assumides per cada esquema. En aquesta tesi hi ha tres fonts d’error que s´on analitzades: i) l’error degut a la discretitzaci´o vertical de l’atmosfera en un conjunt d’estrats que s’assumeixen homogenis (error de truncament, Etrun), ii) l’error com a resultat d’una repre- sentaci´o insuficient de l’estrat entre el cim del model (TOM) i el cim de l’atmosfera (TOA), anomenat error de TOM Etom, i iii) l’error degut a les simplificacions i a les parametritzacions f´ısiques de l’RTE, definit com a error físic, Ephys. Per tal d’evitar la incertesa introdu¨ıda pels altres components del model, el codi font de cadas- cun dels sis esquemes solars ha estat separat del model i adaptat per treballar amb perfils verticals 1-dimensionals. Mitjan¸cant aquest m`etode, les habilitats dels esquemes solars poden ´esser anal- itzades sota condicions d’entrada id`entiques. D’una banda l’error de TOM i el de truncament s’analitzen a partir de perfils ideals. De l’altra, l’error f´ısic s’evalua prenent dades de radiosondatge com a perfil vertical i comparant les sortides dels esquemes radiatius amb mesures en superf´ıcie. Els resultats d’aquesta tesi mostren que l’Etom esdev´e negligible per la majoria d’aplicacions de mesoscala. Per configuracions t´ıpiques del model, l’Etrun en condicions de cel ser`e es troba al voltant de l’1.1%, el 0.9% i el 4.9% per la GHI, DHI i DIF, respectivament. En el cas amb nu´vols augmenta de forma significativa. L’estudi de l’Ephys mostra una relaci´o significativa amb el contingut de vapor d’aigua i els aerosols.
Kim, Byungyu. « Solar Energy Generation Forecasting and Power Output Optimization of Utility Scale Solar Field ». DigitalCommons@CalPoly, 2020. https://digitalcommons.calpoly.edu/theses/2149.
Texte intégralD, Pepe. « New techniques for solar power forecasting and building energy management ». Doctoral thesis, Università di Siena, 2019. http://hdl.handle.net/11365/1072873.
Texte intégralRudd, Timothy Robert. « BENEFITS OF NEAR-TERM CLOUD LOCATION FORECASTING FOR LARGE SOLAR PV ». DigitalCommons@CalPoly, 2011. https://digitalcommons.calpoly.edu/theses/597.
Texte intégralUwamahoro, Jean. « Forecasting solar cycle 24 using neural networks ». Thesis, Rhodes University, 2009. http://hdl.handle.net/10962/d1005253.
Texte intégralSfetsos, Athanasios. « Time series forecasting of wind speed and solar radiation for renewable energy sources ». Thesis, Imperial College London, 1999. http://ethos.bl.uk/OrderDetails.do?uin=uk.bl.ethos.313886.
Texte intégralFerrer, Martínez Claudia. « Machine Learning for Solar Energy Prediction ». Thesis, Högskolan i Gävle, Avdelningen för elektronik, matematik och naturvetenskap, 2018. http://urn.kb.se/resolve?urn=urn:nbn:se:hig:diva-27423.
Texte intégralMohammed, Kadhim Nada. « Creating 3D city models from satellite imagery for integrated assessment and forecasting of solar energy ». Thesis, Cardiff University, 2018. http://orca.cf.ac.uk/109232/.
Texte intégralUppling, Hugo, et Adam Eriksson. « Single and multiple step forecasting of solar power production : applying and evaluating potential models ». Thesis, Uppsala universitet, Institutionen för teknikvetenskaper, 2019. http://urn.kb.se/resolve?urn=urn:nbn:se:uu:diva-384340.
Texte intégralDe, Jong Pieter. « Forecasting, integration, and storage of renewable energy generation in the Northeast of Brazil ». Escola Politécnica, 2017. http://repositorio.ufba.br/ri/handle/ri/24167.
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CAPES e FAPESB.
As a result of global climate change, during the coming decades less rainfall and higher temperatures are projected for the Brazilian Northeast (NE). Consequently these regional climatic changes could severely impact hydroelectric generation in the NE as well as influence solar and wind power potential. The ongoing drought in the Brazilian NE region has caused hydroelectric generation to decline substantially during the last 5 years and in 2016 hydroelectricity only supplied 25% of the NE’s total demand. In contrast, wind power supplied 30% of demand and is expected to generate 55-60% of the NE’s electricity supply by 2020. Therefore, this paper is focused on both short term forecasting and long-term projections of renewable energy generation and resource availability. It also explores the economic, environmental and technical feasibility of renewable energy integration in the NE region of Brazil. First, the long-term impacts of climate change on the NE region’s hydroelectric and wind energy production are analysed. Particular attention is paid to the long-term projections of annual rainfall and streamflow in the São Francisco basin which could decline by approximately 47% and 80%, respectively, by 2050. On the other hand, wind energy potential is projected to increase substantially during the same period. This thesis also estimates the economic, social, and environmental viability of renewable and non-renewable generation technologies in Brazil. The Levelised Cost of Electricity (LCOE) including externalities is calculated for several different case study power plants, the majority of which are located in the Brazilian NE. It was found that wind power becomes the cheapest generation technology in the NE region, once all externality and transmission line costs are taken into consideration. The LCOE for the entire Northeast’s generation matrix is calculated for various configurations, including scenarios in which hydroelectric generation is restricted due to drought conditions. It was concluded that a generation mix in which wind power replaces all fossil fuel generation by 2020, could feasibly reduce the overall LCOE in the region by approximately 46% and substantially decrease CO2eq emissions. Two different methods are used to examine the limits of integrating high penetrations of variable renewable generation technologies into a power system with a large proportion of hydroelectric capacity. In the first method existing wind generation data from 16 wind farms is extrapolated in time and space, while the second method uses a numerical weather prediction model to simulate future wind energy generation in the NE region. Considering the minimum generation requirements of the São Francisco’s hydroelectric dams, the maximum wind energy penetration in the NE region is estimated to be approximately 50% before significant amounts of energy would need to be curtailed or exported to other Brazilian regions. Finally, this thesis reviews additional literature on energy storage and the impact of large scale variable renewable energy integration on grid stability and power quality. It was found that there are several existing technologies such as power factor and voltage regulation devices that can resolve these issues.
Como consequência da mudança climática global, nas próximas décadas menos precipitação e temperaturas mais altas são projetados para Nordeste (NE) do Brasil. Consequentemente, essas mudanças climáticas regionais podem afetar severamente a geração hidrelétrica no NE, bem como influenciar o potencial de energia solar e eólica. A seca atual nessa região do Brasil fez com que a geração hidrelétrica caísse substancialmente durante os últimos 5 anos e em 2016, as usinas hidrelétricas apenas forneceram 25% da demanda total do NE. Em contraste, a energia eólica forneceu 30% da demanda e deverá gerar 55-60% do fornecimento de energia elétrica do NE até 2020. Portanto, este trabalho está focado tanto na previsão a curto quanto projeções a longo prazo da geração de energia renovável e disponibilidade de recursos. Ele também explora a viabilidade econômica, ambiental e técnica da integração de energias renováveis na região NE. Primeiramente, os impactos de longo prazo das mudanças climáticas na produção hidrelétrica e eólica da região NE são analisados. Especial atenção é dada às projeções de longo prazo de precipitação anual e fluxo na bacia do São Francisco, que podem diminuir em aproximadamente 47% e 80%, respectivamente, até 2050. Por outro lado, prevê-se que o potencial da energia eólica aumente substancialmente durante o mesmo período. Esta tese também estima a viabilidade econômica, social e ambiental das tecnologias de geração renováveis e não-renováveis no Brasil. O custo nivelado de energia elétrica (LCOE), incluindo externalidades, é calculado para diversas usinas de estudo de caso, a maioria localizada no NE. Verificou-se que, a energia eólica se torna a tecnologia de geração mais barata na região NE, uma vez que todos os custos de externalidades e de linhas de transmissão são levados em consideração. O LCOE para a matriz de geração do Nordeste é calculado para várias configurações, incluindo cenários em que a geração hidrelétrica é restrita devido às condições de seca. Concluiu-se que, uma mistura de geração em que a energia eólica substitui toda a geração de combustíveis fósseis até 2020, poderia reduzir o LCOE na região em aproximadamente 46% e diminuir substancialmente as emissões de CO2eq. Dois métodos diferentes são usados para examinar os limites da integração de altas penetrações de tecnologias de geração renovável variáveis em um sistema de energia com uma grande proporção de capacidade hidrelétrica. No primeiro método, dados de geração eólica existentes de 16 parques eólicos são extrapolados no tempo e no espaço, enquanto o segundo método utiliza um modelo de previsão numérica de tempo para simular a futura geração de energia eólica na região NE. Considerando as exigências mínimas de geração das hidrelétricas do São Francisco, estima-se que a penetração máxima de energia eólica na região NE seja de aproximadamente 50% antes que quantidades significativas de energia precisem ser desperdiçadas ou exportadas para outras regiões brasileiras. Finalmente, esta tese examina literatura adicional sobre armazenamento de energia e o impacto da integração de energia renovável variável em larga escala na estabilidade da rede elétrica e na qualidade da energia. Verificou-se que existem várias tecnologias existentes, como dispositivos de regulação de fator de potência e tensão que podem resolver estes problemas.
Livres sur le sujet "SOLAR ENERGY FORECASTING"
United States. Bureau of Labor Statistics, dir. Careers in solar power. Washington, D.C.] : U.S. Bureau of Labor Statistics, 2011.
Trouver le texte intégralInvesting in solar stocks : An investor's guide to winning in the global renewable energy market. New York : McGraw-Hill, 2009.
Trouver le texte intégralPaulescu, Marius. Weather Modeling and Forecasting of PV Systems Operation. London : Springer London, 2013.
Trouver le texte intégralNational Renewable Energy Laboratory (U.S.) et IEEE Photovoltaic Specialists Conference (37th : 2011 : Seattle, Wash.), dir. An economic analysis of photovoltaics versus traditional energy sources : Where are we now and where might we be in the near future ? : preprint. Golden, Colo.] : National Renewable Energy Laboratory, 2011.
Trouver le texte intégralSolar Energy Technologies Program (U.S.), National Renewable Energy Laboratory (U.S.) et IEEE Photovoltaic Specialists Conference (37th : 2011 : Seattle, Wash.), dir. An economic analysis of photovoltaics versus traditional energy sources : Where are we now and where might we be in the near future ? [Golden, Colo.] : National Renewable Energy Laboratory, U.S. Dept. of Energy, Office of Energy Efficienty and Renewable Energy, 2011.
Trouver le texte intégralLipták, Béla G. Post-oil energy technology : The world's first solar-hydrogen demonstration power plant. Boca Raton : CRC Press, 2009.
Trouver le texte intégralEuropean Commission. Directorate-General for Energy et European Photovoltaic Industry Association, dir. Photovoltaics in 2010. Luxembourg : Office for Official Publications of the European Communities, 1996.
Trouver le texte intégralLiptak, Bela G. Post-oil energy technology : After the age of fossil fuels. Boca Raton, Fl : Taylor & Francis, 2008.
Trouver le texte intégralKleissl, Jan. Solar Energy Forecasting and Resource Assessment. Elsevier Science & Technology Books, 2013.
Trouver le texte intégralSolar Energy Forecasting and Resource Assessment. Elsevier, 2013. http://dx.doi.org/10.1016/c2011-0-07022-9.
Texte intégralChapitres de livres sur le sujet "SOLAR ENERGY FORECASTING"
Gupta, Anuj, Kapil Gupta et Sumit Saroha. « Solar Energy Radiation Forecasting Method ». Dans Smart Technologies for Energy and Environmental Sustainability, 105–29. Cham : Springer International Publishing, 2021. http://dx.doi.org/10.1007/978-3-030-80702-3_7.
Texte intégralShareef Syed, Mahaboob, Ch V. Suresh, B. Sreenivasa Raju, M. Ravindra Babu et Y. S. Kishore Babu. « Forecasting of Wind Power Using Hybrid Machine Learning Approach ». Dans Wind and Solar Energy Applications, 27–34. Boca Raton : CRC Press, 2023. http://dx.doi.org/10.1201/9781003321897-3.
Texte intégralMasoom, Akriti, Yashwant Kashyap et Ankit Bansal. « Solar Radiation Assessment and Forecasting Using Satellite Data ». Dans Energy, Environment, and Sustainability, 45–71. Singapore : Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-3302-6_3.
Texte intégralDahl, Astrid, et Edwin Bonilla. « Scalable Gaussian Process Models for Solar Power Forecasting ». Dans Data Analytics for Renewable Energy Integration : Informing the Generation and Distribution of Renewable Energy, 94–106. Cham : Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-71643-5_9.
Texte intégralSangle, Ashok S., et Prapti D. Deshmukh. « Forecasting Solar Energy on Time Frame : A Review ». Dans Rising Threats in Expert Applications and Solutions, 427–37. Singapore : Springer Nature Singapore, 2022. http://dx.doi.org/10.1007/978-981-19-1122-4_45.
Texte intégralGandhi, R. R. Rubia, C. Kathirvel, R. Mohan Kumar et M. Siva Ramkumar. « Solar energy forecasting architecture using deep learning models ». Dans Machine Learning and the Internet of Things in Solar Power Generation, 105–21. Boca Raton : CRC Press, 2023. http://dx.doi.org/10.1201/9781003302964-6.
Texte intégralManjarres, Diana, Ricardo Alonso, Sergio Gil-Lopez et Itziar Landa-Torres. « Solar Energy Forecasting and Optimization System for Efficient Renewable Energy Integration ». Dans Data Analytics for Renewable Energy Integration : Informing the Generation and Distribution of Renewable Energy, 1–12. Cham : Springer International Publishing, 2017. http://dx.doi.org/10.1007/978-3-319-71643-5_1.
Texte intégralBadescu, Viorel. « Available Solar Energy and Weather Forecasting on Mars Surface ». Dans Mars, 25–66. Berlin, Heidelberg : Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03629-3_2.
Texte intégralŞahinbaş, Kevser. « Comparative Study of the Forecasting Solar Energy Generation in Istanbul ». Dans Circular Economy and the Energy Market, 185–99. Cham : Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-13146-2_15.
Texte intégralMoradzadeh, Arash, Armin Hosseini Rezaei Asl, Morteza Nazari-Heris, Kazem Zare et Behnam Mohammadi-Ivatloo. « Deep Learning-Assisted Solar Radiation Forecasting for Photovoltaic Power Generation Management in Buildings ». Dans Renewable Energy for Buildings, 47–59. Cham : Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-08732-5_3.
Texte intégralActes de conférences sur le sujet "SOLAR ENERGY FORECASTING"
« Multivariate forecasting of solar energy ». Dans 20th International Congress on Modelling and Simulation (MODSIM2013). Modelling and Simulation Society of Australia and New Zealand (MSSANZ), Inc., 2013. http://dx.doi.org/10.36334/modsim.2013.g1.boland.
Texte intégral« Probabilistic forecasting for solar energy ». Dans 25th International Congress on Modelling and Simulation. Modelling and Simulation Society of Australia and New Zealand, 2023. http://dx.doi.org/10.36334/modsim.2023.boland.
Texte intégralJascourt, Stephen D., Daniel Kirk-Davidhoff et Christopher Cassidy. « Forecasting Solar Power and Irradiance – Lessons from Real-World Experiences ». Dans American Solar Energy Society National Solar Conference 2016. Freiburg, Germany : International Solar Energy Society, 2016. http://dx.doi.org/10.18086/solar.2016.01.15.
Texte intégralHenze, Gregor P. « Parametric Study of a Simplified Ice Storage Model Operating Under Conventional and Optimal Control Strategies ». Dans ASME Solar 2002 : International Solar Energy Conference. ASMEDC, 2002. http://dx.doi.org/10.1115/sed2002-1039.
Texte intégralKumar, Karan, et Nipun Batra. « Solar Energy Forecasting Using Machine Learning ». Dans CoDS COMAD 2020 : 7th ACM IKDD CoDS and 25th COMAD. New York, NY, USA : ACM, 2020. http://dx.doi.org/10.1145/3371158.3371212.
Texte intégralMarquez, Ricardo, et Carlos F. M. Coimbra. « A Novel Metric for Evaluation of Solar Forecasting Models ». Dans ASME 2011 5th International Conference on Energy Sustainability. ASMEDC, 2011. http://dx.doi.org/10.1115/es2011-54519.
Texte intégralManur, Anusha, Maitreyee Marathe, Ashray Manur, Abhishek Ramachandra, Shamsundar Subbarao et Giri Venkataramanan. « Smart Solar Home System with Solar Forecasting ». Dans 2020 IEEE International Conference on Power Electronics, Smart Grid and Renewable Energy (PESGRE). IEEE, 2020. http://dx.doi.org/10.1109/pesgre45664.2020.9070340.
Texte intégralAmreen, T. Sana, Radharani Panigrahi et N. R. Patne. « Solar Power Forecasting Using Hybrid Model ». Dans 2023 5th International Conference on Energy, Power and Environment : Towards Flexible Green Energy Technologies (ICEPE). IEEE, 2023. http://dx.doi.org/10.1109/icepe57949.2023.10201483.
Texte intégralKhoie, Rahim, et Antonio Calderon. « Forecasting Carbon Emissions in States of Hawaii, California, Colorado, and Florida ; The Effects of States’ Renewable Portfolio Standards ». Dans American Solar Energy Society National Solar Conference 2018. Freiburg, Germany : International Solar Energy Society, 2018. http://dx.doi.org/10.18086/solar.2018.01.04.
Texte intégralSiddhant, Kumar, Harshit Garg, Ajay Saha, Nitin Singh, Niraj Kumar Choudhary et Deepak Kumar Singh. « Solar Energy Forecasting using Artificial Neural Network ». Dans 2022 IEEE Students Conference on Engineering and Systems (SCES). IEEE, 2022. http://dx.doi.org/10.1109/sces55490.2022.9887754.
Texte intégralRapports d'organisations sur le sujet "SOLAR ENERGY FORECASTING"
Stoffel, Thomas. U.S. Department of Energy Workshop Report : Solar Resources and Forecasting. Office of Scientific and Technical Information (OSTI), juin 2012. http://dx.doi.org/10.2172/1047954.
Texte intégralSengupta, Manajit, et Craig Turchi. Australian Solar Energy Forecasting System (ASEFS) : Cooperative Research and Development Final Report, CRADA Number CRD-14-541. Office of Scientific and Technical Information (OSTI), août 2020. http://dx.doi.org/10.2172/1659797.
Texte intégralBryce, Richard, Grant Buster, Kate Doubleday, Cong Feng, Ross Ring-Jarvi, Michael Rossol, Flora Zhang et Bri-Mathias Hodge. Solar PV, Wind Generation, and Load Forecasting Dataset for ERCOT 2018 : Performance-Based Energy Resource Feedback, Optimization, and Risk Management (P.E.R.F.O.R.M.). Office of Scientific and Technical Information (OSTI), mai 2023. http://dx.doi.org/10.2172/1972698.
Texte intégralBARKHATOV, NIKOLAY, et SERGEY REVUNOV. A software-computational neural network tool for predicting the electromagnetic state of the polar magnetosphere, taking into account the process that simulates its slow loading by the kinetic energy of the solar wind. SIB-Expertise, décembre 2021. http://dx.doi.org/10.12731/er0519.07122021.
Texte intégralWerth, D., et R. Nichols. Advanced Cloud Forecasting for Solar Energy’s Impact on Grid Modernization. Office of Scientific and Technical Information (OSTI), septembre 2017. http://dx.doi.org/10.2172/1395968.
Texte intégral