Photovoltaic power predictor module based on historical production and weather conditions data
In recent years the demand for electrical energy has increased significantly. Usually, the electrical grid covers this demand. However, this fuel energy is known for its significant carbon footprint. For that reason, different mechanisms to bring cleaner energies have been explored, like hydraulic,...
- Autores:
-
Martinez, Elizabeth
Cuadrado, Juan
Martinez-Santos, Juan Carlos
- Tipo de recurso:
- Fecha de publicación:
- 2022
- Institución:
- Universidad Tecnológica de Bolívar
- Repositorio:
- Repositorio Institucional UTB
- Idioma:
- eng
- OAI Identifier:
- oai:repositorio.utb.edu.co:20.500.12585/12588
- Acceso en línea:
- https://hdl.handle.net/20.500.12585/12588
https://doi.org/10.1007/978-3-031-20611-5_38
- Palabra clave:
- Condition monitoring
Deep learning
Energy production
Forecasting
Photo voltaic
LEMB
- Rights
- openAccess
- License
- http://purl.org/coar/access_right/c_abf2
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| dc.title.spa.fl_str_mv |
Photovoltaic power predictor module based on historical production and weather conditions data |
| title |
Photovoltaic power predictor module based on historical production and weather conditions data |
| spellingShingle |
Photovoltaic power predictor module based on historical production and weather conditions data Condition monitoring Deep learning Energy production Forecasting Photo voltaic LEMB |
| title_short |
Photovoltaic power predictor module based on historical production and weather conditions data |
| title_full |
Photovoltaic power predictor module based on historical production and weather conditions data |
| title_fullStr |
Photovoltaic power predictor module based on historical production and weather conditions data |
| title_full_unstemmed |
Photovoltaic power predictor module based on historical production and weather conditions data |
| title_sort |
Photovoltaic power predictor module based on historical production and weather conditions data |
| dc.creator.fl_str_mv |
Martinez, Elizabeth Cuadrado, Juan Martinez-Santos, Juan Carlos |
| dc.contributor.author.none.fl_str_mv |
Martinez, Elizabeth Cuadrado, Juan Martinez-Santos, Juan Carlos |
| dc.subject.keywords.spa.fl_str_mv |
Condition monitoring Deep learning Energy production Forecasting Photo voltaic |
| topic |
Condition monitoring Deep learning Energy production Forecasting Photo voltaic LEMB |
| dc.subject.armarc.none.fl_str_mv |
LEMB |
| description |
In recent years the demand for electrical energy has increased significantly. Usually, the electrical grid covers this demand. However, this fuel energy is known for its significant carbon footprint. For that reason, different mechanisms to bring cleaner energies have been explored, like hydraulic, wind, thermal, and one of the most popular solar energy. Although solar energy is abundant and environmentally friendly, the photovoltaic energy that comes from the sun, solar production is subject to different external perturbations, such as environmental conditions. Therefore it has been necessary to develop other methods based on statistics, machine learning, or deep learning to make solar forecasting and predict production and weather conditions. Specifically, this work proposes an evaluation of three different deep learning models to predict irradiance, temperature, and production of a photovoltaic system located in the city of Cartagena, Colombia. Those are irradiance and temperature using the historical data on production and weather conditions. This data has been registered on a web platform for seven months, from January 1, 2022, until June 28, 2022. |
| publishDate |
2022 |
| dc.date.issued.none.fl_str_mv |
2022-11-22 |
| dc.date.accessioned.none.fl_str_mv |
2023-12-11T12:33:25Z |
| dc.date.available.none.fl_str_mv |
2023-12-11T12:33:25Z |
| dc.date.submitted.none.fl_str_mv |
2023-12-09 |
| dc.type.coarversion.fl_str_mv |
http://purl.org/coar/version/c_b1a7d7d4d402bcce |
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http://purl.org/coar/resource_type/c_3248 |
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info:eu-repo/semantics/bookPart |
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info:eu-repo/semantics/draft |
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http://purl.org/coar/resource_type/c_2df8fbb1 |
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draft |
| dc.identifier.citation.spa.fl_str_mv |
Martinez, E., Cuadrado, J., & Martinez-Santos, J. C. (2022, November). Photovoltaic Power Predictor Module Based on Historical Production and Weather Conditions Data. In Workshop on Engineering Applications (pp. 461-472). Cham: Springer Nature Switzerland. |
| dc.identifier.uri.none.fl_str_mv |
https://hdl.handle.net/20.500.12585/12588 |
| dc.identifier.doi.none.fl_str_mv |
https://doi.org/10.1007/978-3-031-20611-5_38 |
| dc.identifier.instname.spa.fl_str_mv |
Universidad Tecnológica de Bolívar |
| dc.identifier.reponame.spa.fl_str_mv |
Repositorio Universidad Tecnológica de Bolívar |
| identifier_str_mv |
Martinez, E., Cuadrado, J., & Martinez-Santos, J. C. (2022, November). Photovoltaic Power Predictor Module Based on Historical Production and Weather Conditions Data. In Workshop on Engineering Applications (pp. 461-472). Cham: Springer Nature Switzerland. Universidad Tecnológica de Bolívar Repositorio Universidad Tecnológica de Bolívar |
| url |
https://hdl.handle.net/20.500.12585/12588 https://doi.org/10.1007/978-3-031-20611-5_38 |
| dc.language.iso.spa.fl_str_mv |
eng |
| language |
eng |
| dc.rights.coar.fl_str_mv |
http://purl.org/coar/access_right/c_abf2 |
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info:eu-repo/semantics/openAccess |
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openAccess |
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http://purl.org/coar/access_right/c_abf2 |
| dc.format.extent.none.fl_str_mv |
12 páginas |
| dc.format.mimetype.spa.fl_str_mv |
application/pdf |
| dc.publisher.place.spa.fl_str_mv |
Cartagena de Indias |
| dc.publisher.discipline.spa.fl_str_mv |
Ingeniería de Sistemas y Computación |
| institution |
Universidad Tecnológica de Bolívar |
| bitstream.url.fl_str_mv |
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Martinez, Elizabeth4ebda059-55c6-4e72-8ce5-81181da731b4Cuadrado, Juan73b693c6-9993-4025-9268-0f1bbe13b105Martinez-Santos, Juan Carlos5c958644-c78d-401d-8ba9-bbd39fe773182023-12-11T12:33:25Z2023-12-11T12:33:25Z2022-11-222023-12-09Martinez, E., Cuadrado, J., & Martinez-Santos, J. C. (2022, November). Photovoltaic Power Predictor Module Based on Historical Production and Weather Conditions Data. In Workshop on Engineering Applications (pp. 461-472). Cham: Springer Nature Switzerland.https://hdl.handle.net/20.500.12585/12588https://doi.org/10.1007/978-3-031-20611-5_38Universidad Tecnológica de BolívarRepositorio Universidad Tecnológica de BolívarIn recent years the demand for electrical energy has increased significantly. Usually, the electrical grid covers this demand. However, this fuel energy is known for its significant carbon footprint. For that reason, different mechanisms to bring cleaner energies have been explored, like hydraulic, wind, thermal, and one of the most popular solar energy. Although solar energy is abundant and environmentally friendly, the photovoltaic energy that comes from the sun, solar production is subject to different external perturbations, such as environmental conditions. Therefore it has been necessary to develop other methods based on statistics, machine learning, or deep learning to make solar forecasting and predict production and weather conditions. Specifically, this work proposes an evaluation of three different deep learning models to predict irradiance, temperature, and production of a photovoltaic system located in the city of Cartagena, Colombia. Those are irradiance and temperature using the historical data on production and weather conditions. This data has been registered on a web platform for seven months, from January 1, 2022, until June 28, 2022.12 páginasapplication/pdfengPhotovoltaic power predictor module based on historical production and weather conditions datainfo:eu-repo/semantics/bookPartinfo:eu-repo/semantics/drafthttp://purl.org/coar/resource_type/c_2df8fbb1http://purl.org/coar/version/c_b1a7d7d4d402bccehttp://purl.org/coar/resource_type/c_3248Condition monitoringDeep learningEnergy productionForecastingPhoto voltaicLEMBinfo:eu-repo/semantics/openAccesshttp://purl.org/coar/access_right/c_abf2Cartagena de IndiasIngeniería de Sistemas y ComputaciónPúblico generalF. Dincer, “The analysis on wind energy electricity generation status, potential and policies in the world,” Renewable and sustainable energy reviews, vol. 15, no. 9, pp. 5135–5142, 2011.P. E. Brockway, A. Owen, L. I. Brand-Correa, and L. Hardt, “Estimation of global final-stage energy-return-on-investment for fossil fuels with comparison to renew able energy sources,” Nature Energy, vol. 4, no. 7, pp. 612–621, 2019.R. Aichele and G. Felbermayr, “Kyoto and the carbon footprint of nations,” Jour nal of Environmental Economics and Management, vol. 63, no. 3, pp. 336–354, 2012.K. Li, H. Bian, C. Liu, D. Zhang, and Y. Yang, “Comparison of geothermal with solar and wind power generation systems,” Renewable and Sustainable Energy Re views, vol. 42, pp. 1464–1474, 2015.R. Li, H.-N. Wang, H. He, Y.-M. Cui, and Z.-L. Du, “Support vector machine combined with k-nearest neighbors for solar flare forecasting,” Chinese Journal of Astronomy and Astrophysics, vol. 7, no. 3, p. 441, 2007.F. O. Hocao˘glu, O. N. Gerek, and M. Kurban, “Hourly solar radiation forecasting ¨ using optimal coefficient 2-d linear filters and feed-forward neural networks,” Solar energy, vol. 82, no. 8, pp. 714–726, 2008.Z. Pang, F. Niu, and Z. O’Neill, “Solar radiation prediction using recurrent neural network and artificial neural network: A case study with comparisons,” Renewable Energy, vol. 156, pp. 279–289, 2020.Y. Jung, J. Jung, B. Kim, and S. Han, “Long short-term memory recurrent neural network for modeling temporal patterns in long-term power forecasting for solar pv facilities: Case study of south korea,” Journal of Cleaner Production, vol. 250, p. 119476, 2020.. G. A. de Melo, D. N. Sugimoto, P. M. Tasinaffo, A. H. M. Santos, A. M. Cunha, and L. A. V. Dias, “A new approach to river flow forecasting: Lstm and gru multivariate models,” IEEE Latin America Transactions, vol. 17, no. 12, pp. 1978–1986, 2019.M. Ajith and M. Mart´ınez-Ram´on, “Deep learning based solar radiation micro forecast by fusion of infrared cloud images and radiation data,” Applied Energy, vol. 294, p. 117014, 2021.H. Wang, Z. Lei, X. Zhang, B. Zhou, and J. Peng, “A review of deep learning for renewable energy forecasting,” Energy Conversion and Management, vol. 198, p. 111799, 2019.W. Li, H. Wu, N. Zhu, Y. Jiang, J. Tan, and Y. Guo, “Prediction of dissolved oxy gen in a fishery pond based on gated recurrent unit (gru),” Information Processing in Agriculture, vol. 8, no. 1, pp. 185–193, 2021.T. Yang, L. Zhao, W. Li, and A. Y. Zomaya, “Reinforcement learning in sustainable energy and electric systems: A survey,” Annual Reviews in Control, vol. 49, pp. 145– 163, 2020K. Cho, B. Van Merri¨enboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, and Y. Bengio, “Learning phrase representations using rnn encoder-decoder for statistical machine translation,” arXiv preprint arXiv:1406.1078, 2014http://purl.org/coar/resource_type/c_c94fORIGINALPhotovoltaic_Power_Predictor_Module_based_on_Historical_Production_and_Weather_Conditions_Data_WEA_2022.pdfPhotovoltaic_Power_Predictor_Module_based_on_Historical_Production_and_Weather_Conditions_Data_WEA_2022.pdfapplication/pdf3183765https://repositorio.utb.edu.co/bitstream/20.500.12585/12588/1/Photovoltaic_Power_Predictor_Module_based_on_Historical_Production_and_Weather_Conditions_Data_WEA_2022.pdf938b603aaf9c8ab6e1383d039a5a65e9MD51LICENSElicense.txtlicense.txttext/plain; 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