Method for estimating the wind power micro and minigeneration applied to a city with a subtropical climate in south America




Wind energy potential; Mapping; QGis; Low heights; Different heights.


The search for renewable resources has become necessary for sustainable development. Wind energy is a clean energy source and of global importance, but most studies refer to high altitude and are carried out by researchers in developed countries. In this work, the objective was to evaluate the potential of micro and mini-generation (low heights) wind power in Cascavel-PR, a city located in southern Brazil, as an example of the application of the proposed methodology. For this purpose, wind speed and direction data were used, with a historical series of 21 years (1997-2017). The land use and occupation were performed in a semi-automatic way using Sentinel-2 satellite images. To generate the maps, an algorithm was created in Spring software, which correlated the land use and land cover information, the wind speed kriging and the formula coefficient values according to the identified obstacles. With this it was possible to conclude that regarding the period, spring is considered the season of the year with the greatest energy potential, where the highest averages for speed and power were estimated. It was also noted that the 40-meter-high range has the greatest potential and that the prevailing winds come from the northeast.


ABEolica. (2019). Boletim Anual de Geração Eólica 2019.ção-2019.pdf

Almeida, D. L., & Benassi, R. F. (2015). Crise hídrica e de energia elétrica entre 2014- 2015 na região Sudeste. Revista Hipótese, 1(2), 65–76.

Alvares, C. A., Stape, J. L., Sentelhas, P. C., de Moraes Gonçalves, J. L., & Sparovek, G. (2013). Köppen’s climate classification map for Brazil. Meteorologische Zeitschrift, 22(6), 711–728.

Resolução Normativa no 687 de 2015 da ANEEL, Pub. L. No. no 687, Aneel 24 (2015).

Araújo, F. R. P. de, Pereira, M. G., Freitas, M. A. V., da Silva, N. F., & Dantas, E. J. de A. (2021). Bigger Is Not Always Better: Review of Small Wind in Brazil. Energies, 14(4), 976.

Archer, C. L., & Jacobson, M. Z. (2005). Evaluation of global wind power. Journal of Geophysical Research, 110(D12), D12110.

Bittencourt, L., & Cândido, C. (2006). Introdução à ventilação natural (2nd ed.). EDUFAL.

Bolfarine, H., & Bussab, W. O. (2005). Elementos da amostragem. Blücher.

Brewer, C. (The P. S. U., & Harrower, M. (2020). Color Brewer.

Byrne, R., Hewitt, N. J., Griffiths, P., & MacArtain, P. (2019). An assessment of the mesoscale to microscale influences on wind turbine energy performance at a peri-urban coastal location from the Irish wind atlas and onsite LiDAR measurements. Sustainable Energy Technologies and Assessments, 36(September), 100537.

Carta, J. A., Velázquez, S., & Cabrera, P. (2013). A review of measure-correlate-predict (MCP) methods used to estimate long-term wind characteristics at a target site. Renewable and Sustainable Energy Reviews, 27, 362–400.

Chagas, C. C. M., Pereira, M. G., Rosa, L. P., da Silva, N. F., Freitas, M. A. V., & Hunt, J. D. (2020). From megawatts to kilowatts: A review of small wind turbine applications, lessons from the US to Brazil. Sustainability (Switzerland), 12(7).

Chamanehpour, E., Ahmadizadeh, & Akbarpour. (2017). Site selection of wind power plant using multi-criteria decision-making methods in GIS: A case study. Computational Ecology and Software, 7(2), 49–64.

Clarke, L., Edmonds, J., Krey, V., Richels, R., Rose, S., & Tavoni, M. (2009). International climate policy architectures: Overview of the EMF 22 International Scenarios. Energy Economics, 31(SUPPL. 2), 64–81.

Cochran, W. G. (1965). Técnicas de Amostragem.

Cruz, D. T. (2015). Micro e minigeração eólica e solar no Brasil: Propostas para desenvolvimento do setor. In Escola Politécnica da Universidade de São Paulo.

Drechsler, M., Egerer, J., Lange, M., Masurowski, F., Meyerhoff, J., & Oehlmann, M. (2017). Efficient and equitable spatial allocation of renewable power plants at the country scale. Nature Energy, 2(9), 17124.

Emeksiz, C., & Cetin, T. (2019). In case study: Investigation of tower shadow disturbance and wind shear variations effects on energy production, wind speed and power characteristics. Sustainable Energy Technologies and Assessments, 35(June), 148–159.

Friedland, C. J., Joyner, T. A., Massarra, C., Rohli, R. V., Treviño, A. M., Ghosh, S., Huyck, C., & Weatherhead, M. (2017). Isotropic and anisotropic kriging approaches for interpolating surface-level wind speeds across large, geographically diverse regions. Geomatics, Natural Hazards and Risk, 8(2), 207–224.

Garrido-Perez, J. M., Ordóñez, C., Barriopedro, D., García-Herrera, R., & Paredes, D. (2020). Impact of weather regimes on wind power variability in western Europe. Applied Energy, 264(February), 114731.

Giannini, M., Dutra, R. M., & Guedes, V. G. (2013). Estudo prospectivo do mercado de energia eólica de pequeno porte no Brasil. 21.ólica_de_Pequeno _Porte_ no_Brasil.pdf

Golden Software. (1999). Surfer (No. 15-Trial Version).

IPARDES. (2021). Município de cascavel Abril 2021.

Johansson, B., & Chen, D. (2003). The influence of wind and topography on precipitation distribution in Sweden: statistical analysis and modelling. International Journal of Climatology, 23(12), 1523–1535.

Jung, C., & Schindler, D. (2020). The annual cycle and intra-annual variability of the global wind power distribution estimated by the system of wind speed distributions. Sustainable Energy Technologies and Assessments, 42(October).

Lakes Environmental. (2018). WRPlot View (8.0.2).

Lu, X., & McElroy, M. B. (2017). Global Potential for Wind-Generated Electricity. Wind Energy Engineering: A Handbook for Onshore and Offshore Wind Turbines, 106(27), 51–73.

MME. (2007). Atlas do Potencial Eólico do Paraná. ico_do_Estado_do_Parana.pdf

MME. (2018). Balanço Energético Nacional 2018.

Neiva, A. C. de B., Dutra, R. M., Melo, S. R. F. C. de, Guedes, V. G., Cabrera, A. A. M., Almeida, W. G. de, & Braz, R. de O. (2017). Atlas do Potencial Eólico Brasileiro: Simulações 2013. Habitat International, 52.

Nitsche, P. R., Caramori, P. H., Ricce, W. da S., & Pinto, L. F. D. (2019). Atlas Climático do Estado do Paraná.

Nunes, J. E. de O., Neto, J. V. de S., Silva, M. M. de L., Cordeiro, N. M., Pessoa, R. V. S., Barreto, I. D. de C., Bejan, L. B., & Stosic, T. (2021). Wind speed analysis using Markov chain. Research, Society and Development, 10(9), 1–9.

Oksanen, J., Blanchett, F. G., & Kindt, R. et al. (2015). Vegan: community ecology package.

Paraná Weather System. (2020). SIMEPAR.

Pereira, M. G., Da Silva, N. F., De Melo, S. R. F. C., Freitas, M. A. V., & Guedes, V. G. (2018). Integrated assessment of energy potential and social aspects: Case study of small wind in the state of Rio Grande do Norte. SBSE 2018 - 7th Brazilian Electrical Systems Symposium, 1–6.

Prates, J. E., Zaicovski, M. B., & K., G. (2000). Variabilidade temporal e espacial do vento médio e de rajada no Paraná. Encyclopedia of Volcanoes., 1995, 662.

QGIS Development Team. (2019). QGIS (3.4.4-Madeira). General ublic License (GNU).

R Core Team. (2014). R.

Ramachandra, T. V., Hegde, G., & Krishnadas, G. (2014). Potential assessment and decentralized applications of wind energy in Uttara Kannada, Karnataka. International Journal of Renewable Energy Research, 4(1), 1–10.

REN21. (2019). Renewables 2019 Global Status Report Collaborative. In Ren21 (Vol. 105, Issue July).

Ruggiero, S., Varho, V., & Rikkonen, P. (2015). Transition to distributed energy generation in Finland: Prospects and barriers. Energy Policy, 86, 433–443.

Sampaio, K. R. A., & Batista, V. (2021). The current scenario of wind energy production in Brazil : A literature review. Research, Society and Development, 10(1), 1–8.

Silva, G. F. da, Barreto, I. D. de C., & Stosic, T. (2021). Multiscale entropy analysis of wind speed dynamics in Petrolina, Northeast Brazil. Research, Society and Development, 10(1), 1–9.

Sobral, B. S., Oliveira Júnior, J. F. de, Gois, G. de, Terassi, P. M. de B., & Pereira, C. R. (2018). Regime de Vento na Serra do Mar - Rio de Janeiro, Brasil. Revista Brasileira de Meteorologia, 33(3), 441–451.

Tolmasquim, M. T. (2002). Fontes Renováveis de Energia no Brasil. Editora Interciência Ltda.

Tubelis, A., & Nascimento, F. J. L. do. (1980). Meteorologia descritiva: fundamentos e aplicações brasileiras. Nobel.

Vargas, S. A., Esteves, G. R. T., Maçaira, P. M., Bastos, B. Q., Cyrino Oliveira, F. L., & Souza, R. C. (2019). Wind power generation: A review and a research agenda. Journal of Cleaner Production, 218, 850–870.

Wang, Z., Bui, Q., Zhang, B., Nawarathna, C. L. K., & Mombeuil, C. (2021). The nexus between renewable energy consumption and human development in BRICS countries: The moderating role of public debt. Renewable Energy, 165, 381–390.

Weekes, S. M., Tomlin, A. S., Vosper, S. B., Skea, A. K., Gallani, M. L., & Standen, J. J. (2015). Long-term wind resource assessment for small and medium-scale turbines using operational forecast data and measure-correlate-predict. Renewable Energy, 81, 760–769.

Whiteman, D. C. (2000). Mountain Meteorology: Fundamentals and Applications. Oxford University Press.

Wiegleb, G. (1980). Some applications of principal components analysis in vegetation: Ecological research of aquatic communities. Vegetatio, 42(1–3), 67–73.

Wohlfarth, K., Klobasa, M., & Gutknecht, R. (2020). Demand response in the service sector – Theoretical, technical and practical potentials. Applied Energy, 258(October 2019), 114089.




How to Cite

GUICHO, R.; FERREIRA, J. H. D.; MEDEIROS, G.; SIQUEIRA, J. A. C.; SOUZA, S. N. M. de; PRIOR, M. Method for estimating the wind power micro and minigeneration applied to a city with a subtropical climate in south America. Research, Society and Development, [S. l.], v. 10, n. 12, p. e105101220009, 2021. DOI: 10.33448/rsd-v10i12.20009. Disponível em: Acesso em: 4 mar. 2024.



Agrarian and Biological Sciences