Literatura académica sobre el tema "SOLAR ENERGY FORECASTING"
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Artículos de revistas sobre el tema "SOLAR ENERGY FORECASTING"
Sangrody, Hossein, Morteza Sarailoo, Ning Zhou, Nhu Tran, Mahdi Motalleb y Elham Foruzan. "Weather forecasting error in solar energy forecasting". IET Renewable Power Generation 11, n.º 10 (11 de julio de 2017): 1274–80. http://dx.doi.org/10.1049/iet-rpg.2016.1043.
Texto completoChaudhary, Pankaj, Rohith Gattu, Soundarajan Ezekiel y James Allen Rodger. "Forecasting Solar Radiation". Journal of Cases on Information Technology 23, n.º 4 (octubre de 2021): 1–21. http://dx.doi.org/10.4018/jcit.296263.
Texto completoEl hendouzi, Abdelhakim y Abdennaser Bourouhou. "Solar Photovoltaic Power Forecasting". Journal of Electrical and Computer Engineering 2020 (31 de diciembre de 2020): 1–21. http://dx.doi.org/10.1155/2020/8819925.
Texto completoA. G. M. Amarasinghe, P., N. S. Abeygunawardana, T. N. Jayasekara, E. A. J. P. Edirisinghe y S. K. Abeygunawardane. "Ensemble models for solar power forecasting—a weather classification approach". AIMS Energy 8, n.º 2 (2020): 252–71. http://dx.doi.org/10.3934/energy.2020.2.252.
Texto completoPaulescu, Marius, Nicoleta Stefu, Ciprian Dughir, Robert Blaga, Andreea Sabadus, Eugenia Paulescu y Sorin Bojin. "Online Forecasting of the Solar Energy Production". Annals of West University of Timisoara - Physics 60, n.º 1 (1 de agosto de 2018): 104–10. http://dx.doi.org/10.2478/awutp-2018-0011.
Texto completoNath, N. C., W. Sae-Tang y C. Pirak. "Machine Learning-Based Solar Power Energy Forecasting". Journal of the Society of Automotive Engineers Malaysia 4, n.º 3 (1 de septiembre de 2020): 307–22. http://dx.doi.org/10.56381/jsaem.v4i3.25.
Texto completoMadhiarasan, Manoharan, Mohamed Louzazni y 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 de marzo de 2023): 1–10. http://dx.doi.org/10.1155/2023/2554355.
Texto completoAl-Ali, Elham M., Yassine Hajji, Yahia Said, Manel Hleili, Amal M. Alanzi, Ali H. Laatar y Mohamed Atri. "Solar Energy Production Forecasting Based on a Hybrid CNN-LSTM-Transformer Model". Mathematics 11, n.º 3 (28 de enero de 2023): 676. http://dx.doi.org/10.3390/math11030676.
Texto completoChodakowska, Ewa, Joanicjusz Nazarko, Łukasz Nazarko, Hesham S. Rabayah, Raed M. Abendeh y Rami Alawneh. "ARIMA Models in Solar Radiation Forecasting in Different Geographic Locations". Energies 16, n.º 13 (28 de junio de 2023): 5029. http://dx.doi.org/10.3390/en16135029.
Texto completoVennila, C., Anita Titus, T. Sri Sudha, U. Sreenivasulu, N. Pandu Ranga Reddy, K. Jamal, Dayadi Lakshmaiah, P. Jagadeesh y Assefa Belay. "Forecasting Solar Energy Production Using Machine Learning". International Journal of Photoenergy 2022 (30 de abril de 2022): 1–7. http://dx.doi.org/10.1155/2022/7797488.
Texto completoTesis sobre el tema "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.
Texto completoL’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.
Texto completoD, Pepe. "New techniques for solar power forecasting and building energy management". Doctoral thesis, Università di Siena, 2019. http://hdl.handle.net/11365/1072873.
Texto completoRudd, Timothy Robert. "BENEFITS OF NEAR-TERM CLOUD LOCATION FORECASTING FOR LARGE SOLAR PV". DigitalCommons@CalPoly, 2011. https://digitalcommons.calpoly.edu/theses/597.
Texto completoUwamahoro, Jean. "Forecasting solar cycle 24 using neural networks". Thesis, Rhodes University, 2009. http://hdl.handle.net/10962/d1005253.
Texto completoSfetsos, 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.
Texto completoFerrer, 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.
Texto completoMohammed, 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/.
Texto completoUppling, Hugo y 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.
Texto completoDe, 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.
Libros sobre el tema "SOLAR ENERGY FORECASTING"
United States. Bureau of Labor Statistics, ed. Careers in solar power. Washington, D.C.]: U.S. Bureau of Labor Statistics, 2011.
Buscar texto completoInvesting in solar stocks: An investor's guide to winning in the global renewable energy market. New York: McGraw-Hill, 2009.
Buscar texto completoPaulescu, Marius. Weather Modeling and Forecasting of PV Systems Operation. London: Springer London, 2013.
Buscar texto completoNational Renewable Energy Laboratory (U.S.) y IEEE Photovoltaic Specialists Conference (37th : 2011 : Seattle, Wash.), eds. 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.
Buscar texto completoSolar Energy Technologies Program (U.S.), National Renewable Energy Laboratory (U.S.) y IEEE Photovoltaic Specialists Conference (37th : 2011 : Seattle, Wash.), eds. 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.
Buscar texto completoLipták, Béla G. Post-oil energy technology: The world's first solar-hydrogen demonstration power plant. Boca Raton: CRC Press, 2009.
Buscar texto completoEuropean Commission. Directorate-General for Energy y European Photovoltaic Industry Association, eds. Photovoltaics in 2010. Luxembourg: Office for Official Publications of the European Communities, 1996.
Buscar texto completoLiptak, Bela G. Post-oil energy technology: After the age of fossil fuels. Boca Raton, Fl: Taylor & Francis, 2008.
Buscar texto completoKleissl, Jan. Solar Energy Forecasting and Resource Assessment. Elsevier Science & Technology Books, 2013.
Buscar texto completoSolar Energy Forecasting and Resource Assessment. Elsevier, 2013. http://dx.doi.org/10.1016/c2011-0-07022-9.
Texto completoCapítulos de libros sobre el tema "SOLAR ENERGY FORECASTING"
Gupta, Anuj, Kapil Gupta y Sumit Saroha. "Solar Energy Radiation Forecasting Method". En 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.
Texto completoShareef Syed, Mahaboob, Ch V. Suresh, B. Sreenivasa Raju, M. Ravindra Babu y Y. S. Kishore Babu. "Forecasting of Wind Power Using Hybrid Machine Learning Approach". En Wind and Solar Energy Applications, 27–34. Boca Raton: CRC Press, 2023. http://dx.doi.org/10.1201/9781003321897-3.
Texto completoMasoom, Akriti, Yashwant Kashyap y Ankit Bansal. "Solar Radiation Assessment and Forecasting Using Satellite Data". En Energy, Environment, and Sustainability, 45–71. Singapore: Springer Singapore, 2018. http://dx.doi.org/10.1007/978-981-13-3302-6_3.
Texto completoDahl, Astrid y Edwin Bonilla. "Scalable Gaussian Process Models for Solar Power Forecasting". En 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.
Texto completoSangle, Ashok S. y Prapti D. Deshmukh. "Forecasting Solar Energy on Time Frame: A Review". En 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.
Texto completoGandhi, R. R. Rubia, C. Kathirvel, R. Mohan Kumar y M. Siva Ramkumar. "Solar energy forecasting architecture using deep learning models". En 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.
Texto completoManjarres, Diana, Ricardo Alonso, Sergio Gil-Lopez y Itziar Landa-Torres. "Solar Energy Forecasting and Optimization System for Efficient Renewable Energy Integration". En 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.
Texto completoBadescu, Viorel. "Available Solar Energy and Weather Forecasting on Mars Surface". En Mars, 25–66. Berlin, Heidelberg: Springer Berlin Heidelberg, 2009. http://dx.doi.org/10.1007/978-3-642-03629-3_2.
Texto completoŞahinbaş, Kevser. "Comparative Study of the Forecasting Solar Energy Generation in Istanbul". En 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.
Texto completoMoradzadeh, Arash, Armin Hosseini Rezaei Asl, Morteza Nazari-Heris, Kazem Zare y Behnam Mohammadi-Ivatloo. "Deep Learning-Assisted Solar Radiation Forecasting for Photovoltaic Power Generation Management in Buildings". En Renewable Energy for Buildings, 47–59. Cham: Springer International Publishing, 2022. http://dx.doi.org/10.1007/978-3-031-08732-5_3.
Texto completoActas de conferencias sobre el tema "SOLAR ENERGY FORECASTING"
"Multivariate forecasting of solar energy". En 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.
Texto completo"Probabilistic forecasting for solar energy". En 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.
Texto completoJascourt, Stephen D., Daniel Kirk-Davidhoff y Christopher Cassidy. "Forecasting Solar Power and Irradiance – Lessons from Real-World Experiences". En 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.
Texto completoHenze, Gregor P. "Parametric Study of a Simplified Ice Storage Model Operating Under Conventional and Optimal Control Strategies". En ASME Solar 2002: International Solar Energy Conference. ASMEDC, 2002. http://dx.doi.org/10.1115/sed2002-1039.
Texto completoKumar, Karan y Nipun Batra. "Solar Energy Forecasting Using Machine Learning". En CoDS COMAD 2020: 7th ACM IKDD CoDS and 25th COMAD. New York, NY, USA: ACM, 2020. http://dx.doi.org/10.1145/3371158.3371212.
Texto completoMarquez, Ricardo y Carlos F. M. Coimbra. "A Novel Metric for Evaluation of Solar Forecasting Models". En ASME 2011 5th International Conference on Energy Sustainability. ASMEDC, 2011. http://dx.doi.org/10.1115/es2011-54519.
Texto completoManur, Anusha, Maitreyee Marathe, Ashray Manur, Abhishek Ramachandra, Shamsundar Subbarao y Giri Venkataramanan. "Smart Solar Home System with Solar Forecasting". En 2020 IEEE International Conference on Power Electronics, Smart Grid and Renewable Energy (PESGRE). IEEE, 2020. http://dx.doi.org/10.1109/pesgre45664.2020.9070340.
Texto completoAmreen, T. Sana, Radharani Panigrahi y N. R. Patne. "Solar Power Forecasting Using Hybrid Model". En 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.
Texto completoKhoie, Rahim y Antonio Calderon. "Forecasting Carbon Emissions in States of Hawaii, California, Colorado, and Florida; The Effects of States’ Renewable Portfolio Standards". En 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.
Texto completoSiddhant, Kumar, Harshit Garg, Ajay Saha, Nitin Singh, Niraj Kumar Choudhary y Deepak Kumar Singh. "Solar Energy Forecasting using Artificial Neural Network". En 2022 IEEE Students Conference on Engineering and Systems (SCES). IEEE, 2022. http://dx.doi.org/10.1109/sces55490.2022.9887754.
Texto completoInformes sobre el tema "SOLAR ENERGY FORECASTING"
Stoffel, Thomas. U.S. Department of Energy Workshop Report: Solar Resources and Forecasting. Office of Scientific and Technical Information (OSTI), junio de 2012. http://dx.doi.org/10.2172/1047954.
Texto completoSengupta, Manajit y 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), agosto de 2020. http://dx.doi.org/10.2172/1659797.
Texto completoBryce, Richard, Grant Buster, Kate Doubleday, Cong Feng, Ross Ring-Jarvi, Michael Rossol, Flora Zhang y 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), mayo de 2023. http://dx.doi.org/10.2172/1972698.
Texto completoBARKHATOV, NIKOLAY y 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, diciembre de 2021. http://dx.doi.org/10.12731/er0519.07122021.
Texto completoWerth, D. y R. Nichols. Advanced Cloud Forecasting for Solar Energy’s Impact on Grid Modernization. Office of Scientific and Technical Information (OSTI), septiembre de 2017. http://dx.doi.org/10.2172/1395968.
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