PERNAMBUCO SOLARIMETRIC NETWORK
DOI:
https://doi.org/10.59627/cbens.2022.1056Keywords:
Solar Energy, Solarimetry, Solar RadiationAbstract
The Pernambuco Solarimetric Network (REDESOLPE in portuguese) is a project conceived by FACEPE 15/2019 call. This call had the goal for implementing and consolidate multiuser laboratories in Pernambuco State. Those laboratories permit that researchers and companies interested can use its resources to develop research in the State of Pernambuco. This work presents the REDESOLPE consolidating process, that in the beginning it was created with 22 solarimetric stations belonging to INMET, INPE and IFPE. It was installed 13 solarimetric stations under the responsibility of IFPE, due to the cooperation agreement with APAC, 27 stations were incorporated to the network and 13 INMET stations, that are located around the State border, were added, all these activities were performed with the financing from the proposal and correspond to 75 stations with solarimetric and climatologic measurements as global irradiance, air temperature, humidity, and others. It was launched a webpage (www.redesolpe.com.br) were it´s possible for the researchers and companies interested to use those data for scientific works to access free of charge any period of data time from any station that belong to the project, to download data, that are supplied without any treatment or qualification, with the condition to mention in the publications the REDESOLPE as a source of data.
Downloads
References
Aguiar, L. M., Pereira, B., Lauret, P., Díaz, F. e David, M., 2016. Combining solar irradiance measurements, satellite-derived data and a numerical weather prediction model to improve intra-day solar forecasting. Renewable Energy, vol. 97, n. pp. 599-610.
Amanajás, J. C. e Araújo, R. C. M. d., 2008. Uso do método de contingência para análise da eficácia de previsão da precipitação pluviométrica do modelo ETA para o município de Macapá-AP em 2007. Monografia (Licenciatura Plena em Matemática)–Universidade Federal do Amapá, Amapá, vol., n. pp.
Cao, F., Li, H., Yang, T., Li, Y., Zhu, T. e Zhao, L., 2017. Evaluation of diffuse solar radiation models in Northern China: New model establishment and radiation sources comparison. Renewable Energy, vol. 103, n. pp. 708-720.
Dong, Z., Yang, D., Reindl, T. e Walsh, W. M., 2014. Satellite image analysis and a hybrid esss/ann model to forecast solar irradiance in the tropics. Energy Conversion and Management, vol. 79, n. pp. 66-73.
FACEPE, 2019. Edital FACEPE 15/2019 - Apoio à Disponibilização para a Pesquisa de Laboratórios Multiusuários.
Hammer, A., Heinemann, D., Hoyer, C. e Lorenz, E., 2001. Satellite based short-term forecasting of solar irradiance—comparison of methods and error analysis. The 2001 EUMETSAT meteorological satellite data user’s conference.
Inman, R. H., Pedro, H. T. C. e Coimbra, C. F. M., 2013. Solar forecasting methods for renewable energy integration. Progress in Energy and Combustion Science, vol. 39, n. 6, pp. 535-576.
INMET. 2021. Mapa de estações. Disponível em: < https://mapas.inmet.gov.br/ >. Acesso em: 02/10/2021.
Lima, F. J. L. d., 2015. Previsão de Irradiação Solar no Nordeste do Brasil Empregando o Modelo WRF Ajustado por Redes Neurais Artificiais (RNAs). Doutorado, INPE,
Martín, L., Zarzalejo, L. F., Polo, J., Navarro, A., Marchante, R. e Cony, M., 2010. Prediction of global solar irradiance based on time series analysis: application to solar thermal power plants energy production planning. Solar Energy, vol. 84, n. 10, pp. 1772-1781.
Mellit, A. e Pavan, A. M., 2010. A 24-h forecast of solar irradiance using artificial neural network: Application for performance prediction of a grid-connected PV plant at Trieste, Italy. Solar Energy, vol. 84, n. 5, pp. 807-821.
Monjoly, S., André, M., Calif, R. e Soubdhan, T., 2017. Hourly forecasting of global solar radiation based on multiscale decomposition methods: A hybrid approach. Energy, vol. 119, n. pp. 288-298.
Penna, B. R., Silveira, B. B., Gonçalves, L. G. e Souza, S. S. d., 2015. Avaliação da temperatura de brilho nos canais sensíveis à superfície terrestre no sistema G3DVAR do CPTEC/INPE: série NOAA. Revista Brasileira de Meteorologia, vol. 30, n. pp. 340-350.
Perez, R., Ineichen, P., Moore, K., Kmiecik, M., Chain, C., George, R. e Vignola, F., 2002. A new operational model for satellite-derived irradiances: description and validation. Solar Energy, vol. 73, n. 5, pp. 307-317.
Perez, R., Kivalov, S., Schlemmer, J., Hemker, K., Renné, D. e Hoff, T. E., 2010. Validation of short and medium term operational solar radiation forecasts in the US. Solar Energy, vol. 84, n. 12, pp. 2161-2172.
Raphael, C. e Hay, J. E., 1984. An assessment of models which use satellite data to estimate solar irradiance at the earth's surface. Journal of Climate and Applied Meteorology, vol. 23, n. 5, pp. 832-844.
Reikard, G., 2009. Predicting solar radiation at high resolutions: A comparison of time series forecasts. Solar Energy, vol. 83, n. 3, pp. 342-349.
Sabino, E. R. C., de Petribú Brennand, L. J., de Barros, H. F., de Castro Vilela, O. e da Costa, A. C. A., 2017. Previsão em Curto Prazo de Índice de Claridade utilizando Redes Neurais e Variáveis Sazonais. Avances en Energías Renovables y Medio Ambiente, vol. 21, n. pp. 11.39-11.48.
Tarpley, J. D., 1979. Estimating incident solar radiation at the surface from geostationary satellite data. Journal of Applied Meteorology, vol. 18, n. 9, pp. 1172-1181.
Voyant, C., Darras, C., Muselli, M., Paoli, C., Nivet, M.-L. e Poggi, P., 2014. Bayesian rules and stochastic models for high accuracy prediction of solar radiation. Applied Energy, vol. 114, n. pp. 218-226.
