e-ISSN 2231-8526
ISSN 0128-7680
Marco Pereira de Souza, Luis Claudio Bernardo Moura, Carlos Alberto Nunes Cosenza, Silvio de Macedo Amaral, Rodrigo Pestana Cunha Telles, Manuel Oliveira Lemos Alexandre, Silvio Barbosa, Bruno de Sousa Elia, Maria Fernanda Zelaya Correia, Antonio Carlos de Lemos Oliveira, Rodrigo Ventura da Silva and Thais Rodrigues Pinheiro
Pertanika Journal of Science & Technology, Volume 32, Issue 2, March 2024
DOI: https://doi.org/10.47836/pjst.32.2.05
Keywords: GIS, multi-criteria decision making (MCDM), Rio de Janeiro, site selection, solar photovoltaic (PV)
Published on: 26 March 2024
Photovoltaic (PV) energy has become a low-cost, renewable, and environmentally friendly alternative to meet increasing energy demand. Nevertheless, there is still a lack of projects in this field in Brazil. Therefore, this study compares the results of two studies on the optimal site selection of PV in the Brazilian state of Rio de Janeiro. These studies used different methodologies to reach the conclusions and the resulting map. First, the final map of both studies was divided into a grid, and then the results of each cell were weighted for PV site selection. To compare the results using the maps, an intersection of the 10% of the grid cells with the best results from each study was formed. The results showed an 83% similarity between the different Multi-Criteria Decision-Making (MCDM) methods. The other part of the comparison focused on the following rank similarity coefficients: Spearman Correlation Coefficient, WS Coefficient, Spearman Weighted Correlation Coefficient, and Blest Correlation Coefficient. All these coefficients had values greater than 0.9, indicating a high degree of correlation between the results of the studies. Therefore, the two studies have a high degree of similarity and a high potential for installing photovoltaic solar power plants in Rio de Janeiro, especially in its intersection zones.
Abdel-Basset, M., Gamal, A., & ELkomy, O. M. (2021). Hybrid multi-criteria decision making approach for the evaluation of sustainable photovoltaic farms locations. Journal of Cleaner Production, 328(July), Article 129526. https://doi.org/10.1016/j.jclepro.2021.129526
Al-Shamisi, M. H., Assi, A. H., & Hejase, H. A. N. (2013). Artificial neural networks for predicting global solar radiation in Al Ain City - UAE. International Journal of Green Energy, 10(5), 443-456. https://doi.org/10.1080/15435075.2011.641187
Al Garni, H. Z., & Awasthi, A. (2017). Solar PV power plant site selection using a GIS-AHP based approach with application in Saudi Arabia. Applied Energy, 206, 1225-1240. https://doi.org/10.1016/j.apenergy.2017.10.024
Allen, M. R., Pauline Dube, O., Solecki, W., Aragón-Durand, F., Cramer France, W., Humphreys, S., Dasgupta, P., Millar, R., Dube, O., Solecki, W., Aragón-Durand, F., Cramer, W., Humphreys, S., Kainuma, M., Kala, J., Mahowald, N., Mulugetta, Y., Perez, R., Wairiu, M., … & Waterfield, T. (2018). Special Report: Global warming of 1.5 oC. The Intergovernmental Panel on Climate Change. https://www.ipcc.ch/sr15/
Aly, A., Jensen, S. S., & Pedersen, A. B. (2017). Solar power potential of Tanzania: Identifying CSP and PV hot spots through a GIS multicriteria decision making analysis. Renewable Energy, 113, 159-175. https://doi.org/10.1016/J.RENENE.2017.05.077
Asuero, A. G., Sayago, A., & González, A. G. (2006). The correlation coefficient: An overview. Critical Reviews in Analytical Chemistry, 36(1), 41-59. https://doi.org/10.1080/10408340500526766
Bandyopadhyay, S. (2016). Ranking of suppliers with MCDA technique and probabilistic criteria. In 2016 International Conference on Data Science and Engineering (ICDSE) (pp. 1-5). IEEE Publication. https://doi.org/10.1109/ICDSE.2016.7823948
Bishop, M. P., & Giardino, J. R. (2021). Technology-driven geomorphology: introduction and overview. Treatise on Geomorphology, 1, 1-17. https://doi.org/10.1016/B978-0-12-818234-5.00171-1
Blest, D. C. (2000). Theory & methods: Rank correlation - An alternative measure. Australian and New Zealand Journal of Statistics, 42(1), 101-111. https://doi.org/10.1111/1467-842X.00110
Ceballos, B., Lamata, M. T., & Pelta, D. A. (2016). A comparative analysis of multi-criteria decision-making methods. Progress in Artificial Intelligence, 5, 315-322. https://doi.org/10.1007/s13748-016-0093-1
CEPERJ. (2019). Fundação Estadual de Estatísticas, Pesquisas e Formação de Servidores do Estado do Rio de Janeiro. [Foundation for Statistics, Research, and Training of Civil Servants of the State of Rio de Janeiro]. https://www.ceperj.rj.gov.br/wp-content/uploads/2021/07/PIB-ESTADUAL2018.pdf
CEPERJ. (2022a). Histórico e Características | CEPERJ. Fundação Estadual de Estatísticas, Pesquisas e Formação de Servidores Do Estado Do Rio de Janeiro [Foundation for Statistics, Research, and Training of Civil Servants of the State of Rio de Janeiro]. https://www.ceperj.rj.gov.br/?page_id=260
CEPERJ. (2022b). Regiões | CEPERJ. Fundação Estadual de Estatísticas, Pesquisas e Formação de Servidores Do Estado Do Rio de Janeiro [Foundation for Statistics, Research, and Training of Civil Servants of the State of Rio de Janeiro]. https://www.ceperj.rj.gov.br/?page_id=262
Cosenza, C. A. N., Doria, F. A., & Pessôa, L. A. M. (2015). Hierarchy models for the organization of economic spaces. Procedia Computer Science, 55, 82-91. https://doi.org/10.1016/j.procs.2015.07.010
Da Costa, J. P., & Soares, C. (2005). A weighted rank measure of correlation. Australian and New Zealand Journal of Statistics, 47(4), 515-529. https://doi.org/10.1111/j.1467-842X.2005.00413.x
Das, A. K., & Bhuyan, P. K. (2017). Hardcl method for defining LOS criteria of urban streets. International Journal of Civil Engineering, 15, 1077-1086. https://doi.org/10.1007/S40999-017-0207-6
de Souza, M. P., Moura, L. C. B., & Cosenza, C. A. N. (2019). Analysis to determine the most suitable location for a photovoltaic solar plant in the state of Rio De Janeiro, Brazil. International Journal of Development Research, 09(11), Article 17462.
de Souza, M. P., Moura, L. C. B., Cosenza, C. A. N., Brasil, C. N. F., Cosenza, H. J. S. R., Amaral, S. de M., & Dias, S. M. P. (2021a). Analysis to determine the most suitable location for a photovoltaic solar plant using coppe-cosenza method: Case study Rio De Janeiro. International Journal of Development Research, 11(04), 46378-46382.
de Souza, M. P., Moura, L. C. B., & Cosenza, C. A. N. (2021b). Análise para a localização ótima de uma usina solar fotovoltaica no estado do Rio de Janeiro [Analysis for the optimal location of a photovoltaic solar plant in the state of Rio de Janeiro]. Revista Brasileira de Energia, 27(4), 8-37. https://doi.org/10.47168/rbe.v27i4.491
de Souza, M. P., Moura, L. C. B., Cosenza, C. A. N., Dias, S. M. P., & Barata, P. R. (2021c, October 18-21). Determinação da Localização de uma Usina Solar Fotovoltaica com o Auxílio de Método de Decisão Multicritério [Determination of the location of a solar photovoltaic plant with the aid of a multicriteria decision method]. In Proceedings of the National Production Engineering Meeting - Enegep (pp. 1-12). Paraná, Brazil. https://doi.org/10.14488/enegep2021_tn_sto_362_1872_41849
Doorga, J. R. S., Rughooputh, S. D. D. V., & Boojhawon, R. (2019). Multi-criteria GIS-based modelling technique for identifying potential solar farm sites: A case study in Mauritius. Renewable Energy, 133, 1201-1219. https://doi.org/10.1016/j.renene.2018.08.105
EGPEnergia, & PUC-Rio. (2016). Atlas Rio Solar - Atlas Solarimétrico do Estado do Rio de Janeiro [Rio Solar Atlas - Solimeric Atlas of the State of Rio de Janeiro].
EPE. (2020a). Balanço energético nacional 2020 [National energy balance 2020]. Empresa de Pesquisa Energética. https://www.epe.gov.br/pt/publicacoes-dados-abertos/publicacoes/balanco-energetico-nacional-2020
EPE. (2020b). Plano decenal de expanção de energia 2029 [Ten-year energy expansion plan 2029]. Empresa de Pesquisa Energética. https://www.epe.gov.br/pt/publicacoes-dados-abertos/publicacoes/plano-decenal-de-expansao-de-energia-2029
EPE. (2016). Estudos da demanda de energia: Demanda de energia 2050 [Energy Demand Studies: Energy Demand 2050]. https://www.epe.gov.br/sites-pt/publicacoes-dados-abertos/publicacoes/PublicacoesArquivos/publicacao-227/topico-458/DEA 13-15 Demanda de Energia 2050.pdf
Fagin, R., Kumar, R., & Sivakumar, D. (2003). Comparing top k lists. Journal on Discrete Mathematics, 17(1), 134-160. https://doi.org/10.1137/S0895480102412856
Figueira, J., Greco, S., & Ehrogott, M. (2005). Multiple Criteria Decision Analysis: State of the Art Surveys. Springer. https://doi.org/10.1007/b100605
Figueira, J. R., Mousseau, V., & Roy, B. (2016). ELECTRE methods. In S. Greco, M. Ehrgott & J. Figueira (Eds.), Multiple Criteria Decision Analysis. International Series in Operations Research & Management Science (Vol. 233; pp. 155-185). Springer. https://doi.org/10.1007/978-1-4939-3094-4_5
Giamalaki, M., & Tsoutsos, T. (2019). Sustainable siting of solar power installations in the Mediterranean using a GIS/AHP approach. Renewable Energy, 141, 64-75. https://doi.org/10.1016/j.renene.2019.03.100
Guitouni, A., & Martel, J. M. (1998). Tentative guidelines to help choosing an appropriate MCDA method. European Journal of Operational Research, 109(2), 501-521. https://doi.org/10.1016/S0377-2217(98)00073-3
IBGE. (2022). Cidades e Estados [Cities and States]. IBGE. https://www.ibge.gov.br/cidades-e-estados/rj.html
IRENA. (2020). Renewable Power Generation Costs in 2019 - Key Findings. International Renewable Energy Agency. https://www.irena.org/publications/2020/Jun/Renewable-Power-Costs-in-2019
Ishizaka, A., & Siraj, S. (2018). Are multi-criteria decision-making tools useful? An experimental comparative study of three methods. European Journal of Operational Research, 264(2), 462-471. https://doi.org/10.1016/j.ejor.2017.05.041
Ivlev, I., Jablonsky, J., & Kneppo, P. (2016). Multiple-criteria comparative analysis of magnetic resonance imaging systems. International Journal of Medical Engineering and Informatics, 8(2), 124-141. https://doi.org/10.1504/IJMEI.2016.075757
Jain, A., Mehta, R., & Mittal, S. K. (2011). Modeling impact of solar radiation on site selection for solar pv power plants in India. International Journal of Green Energy, 8(4), 486-498. https://doi.org/10.1080/15435075.2011.576293
Janke, J. R. (2010). Multi-criteria GIS modeling of wind and solar farms in Colorado. Renewable Energy, 35(10), 2228-2234. https://doi.org/10.1016/j.renene.2010.03.014
Kizielewicz, B., Wątróbski, J., & Sałabun, W. (2020). Identification of relevant criteria set in the MCDA process - Wind farm location case study. Energies, 13(24), Article 6548. https://doi.org/10.3390/en13246548
Kolios, A., Mytilinou, V., Lozano-Minguez, E., & Salonitis, K. (2016). A comparative study of multiple-criteria decision-making methods under stochastic inputs. Energies, 9(7), 1-21. https://doi.org/10.3390/en9070566
Kwak, Y., Deal, B., & Heavisides, T. (2021). A large scale multi-criteria suitability analysis for identifying solar development potential: A decision support approach for the state of Illinois, USA. Renewable Energy, 177, 554-567. https://doi.org/10.1016/j.renene.2021.05.165
La Camera, F. (2020). Renewable Power Generation Costs in 2019. International Renewable Energy Agency. https://www.irena.org/-/media/Files/IRENA/Agency/Publication/2018/Jan/IRENA_2017_Power_Costs_2018.pdf
Manson, S., Matson, L., Kernik, M., DeLuca, E., Bonsal, D., & Nelson, S. (2017). Mapping, Society, and Technology. Libraries Publishing.
Mulliner, E., Malys, N., & Maliene, V. (2016). Comparative analysis of MCDM methods for the assessment of sustainable housing affordability. Omega, 59(Part B), 146-156. https://doi.org/10.1016/j.omega.2015.05.013
Ohunakin, O. S., & Saracoglu, B. O. (2018). A comparative study of selected multi-criteria decision-making methodologies for location selection of very large concentrated solar power plants in Nigeria. African Journal of Science, Technology, Innovation and Development, 10(5), 551-567. https://doi.org/10.1080/20421338.2018.1495305
Palmer, D., Gottschalg, R., & Betts, T. (2019). The future scope of large-scale solar in the UK: Site suitability and target analysis. Renewable Energy, 133, 1136-1146. https://doi.org/10.1016/j.renene.2018.08.109
Paramasivam, C. R., & Venkatramanan, S. (2019). Chapter 3 - An introduction to various spatial analysis techniques. In GIS and Geostatistical Techniques for Groundwater Science (pp. 23-30). Elsevier. https://doi.org/10.1016/B978-0-12-815413-7.00003-1
Pereira, E. B., Martins, F. R., Gonçalves, A. R., Costa, R. S., Lima, F. J. L. de, Rüther, R., Abreu, S. L. de, Tiepolo, G. M., Pereira, S. V., & Souza, J. G. de. (2017). Atlas Brasileiro Energia Solar 2a Edição [Brazilian Solar Energy Atlas 2nd Edition]. Instituto Nacional de Pesquisas Espaciais.
Qiu, T., Wang, L., Lu, Y., Zhang, M., Qin, W., Wang, S., & Wang, L. (2022). Potential assessment of photovoltaic power generation in China. Renewable and Sustainable Energy Reviews, 154, Article 111900. https://doi.org/10.1016/j.rser.2021.111900
Ramedani, Z., Omid, M., & Keyhani, A. (2013). Modeling solar energy potential in a Tehran province using artificial neural networks. International Journal of Green Energy, 10(4), 427-441. https://doi.org/10.1080/15435075.2011.647172
Razykov, T. M., Ferekides, C. S., Morel, D., Stefanakos, E., Ullal, H. S., & Upadhyaya, H. M. (2011). Solar photovoltaic electricity: Current status and future prospects. Solar Energy, 85(8), 1580-1608. https://doi.org/10.1016/j.solener.2010.12.002
Ribeiro, M. A., & Nunes, N. da S. (2019). Geografia do Estado do Rio de Janeiro [Geography of the State of Rio de Janeiro]. CECIERJ. https://canal.cecierj.edu.br/022020/6a6bfdba31d1653c8e1cb37b757a531a.pdf
Rios, R., & Duarte, S. (2021). Selection of ideal sites for the development of large-scale solar photovoltaic projects through analytical hierarchical process – Geographic information systems (AHP-GIS) in Peru. Renewable and Sustainable Energy Reviews, 149, Article 111310. https://doi.org/10.1016/j.rser.2021.111310
Rodgers, J. L., & Nicewander, W. A. (1988). Thirteen ways to look at the correlation coefficient. American Statistician, 42(1), 59-66. https://doi.org/10.1080/00031305.1988.10475524
Roy, B. (2016). Paradigms and challenges. In S. Greco, M. Ehrgott & J. Figueira (Eds.), Multiple Criteria Decision Analysis (pp 19-39). Springer. https://doi.org/10.1007/978-1-4939-3094-4_2
Sałabun, W., & Piegat, A. (2017). Comparative analysis of MCDM methods for the assessment of mortality in patients with acute coronary syndrome. Artificial Intelligence Review, 48, 557-571. https://doi.org/10.1007/s10462-016-9511-9
Sałabun, W., & Urbaniak, K. (2020). A new coefficient of rankings similarity in decision-making problems. In V. Krzhizhanovskaya, G. Závodszky, M. H. Lees, J. J. Dongarra, P. M. A. Sloot, S. Brissos & J. Teixeira (Eds.), Computational Science - ICCS 2020. ICCS 2020. Lecture Notes in Computer Science (pp. 632-645). Springer. https://doi.org/10.1007/978-3-030-50417-5_47
Sałabun, W., Watróbski, J., & Shekhovtsov, A. (2020). Are MCDA methods benchmarkable? A comparative study of TOPSIS, VIKOR, COPRAS, and PROMETHEE II methods. Symmetry, 12(9), Article 1549. https://doi.org/10.3390/SYM12091549
San Cristóbal, J. R. (2011). Multi-criteria decision-making in the selection of a renewable energy project in Spain: The Vikor method. Renewable Energy, 36(2), 498-502. https://doi.org/10.1016/j.renene.2010.07.031
Sánchez-Lozano, J. M., García-Cascales, M. S., & Lamata, M. T. (2016a). Comparative TOPSIS-ELECTRE TRI methods for optimal sites for photovoltaic solar farms. Case study in Spain. Journal of Cleaner Production, 127, 387-398. https://doi.org/10.1016/j.jclepro.2016.04.005
Sánchez-Lozano, J. M., García-Cascales, M. S., & Lamata, M. T. (2016b). Comparative TOPSIS-ELECTRE TRI methods for optimal sites for photovoltaic solar farms. Case study in Spain. Journal of Cleaner Production, 127, 387-398. https://doi.org/10.1016/j.jclepro.2016.04.005
Schober, P., & Schwarte, L. A. (2018). Correlation coefficients: Appropriate use and interpretation. Anesthesia and Analgesia, 126(5), 1763-1768. https://doi.org/10.1213/ANE.0000000000002864
Scott, L. M. (2015). Spatial pattern, analysis of. In J. D. Wright (Ed), International Encyclopedia of the Social & Behavioral Sciences: Second Edition (Vol. 22). Elsevier. https://doi.org/10.1016/B978-0-08-097086-8.72064-2
Shao, M., Han, Z., Sun, J., Xiao, C., Zhang, S., & Zhao, Y. (2020). A review of multi-criteria decision-making applications for renewable energy site selection. Renewable Energy, 157, 377-403. https://doi.org/10.1016/j.renene.2020.04.137
Shekhovtsov, A., & Kolodziejczyk, J. (2020). Do distance-based multi-criteria decision analysis methods create similar rankings? Procedia Computer Science, 176, 3718-3729. https://doi.org/10.1016/j.procs.2020.09.015
Shieh, G. S. (1998). A weighted Kendall’s tau statistic. Statistics and Probability Letters, 39(1), 17-24. https://doi.org/10.1016/s0167-7152(98)00006-6
Shorabeh, S. N., Firozjaei, M. K., Nematollahi, O., Firozjaei, H. K., & Jelokhani-Niaraki, M. (2019). A risk-based multi-criteria spatial decision analysis for solar power plant site selection in different climates: A case study in Iran. Renewable Energy, 143, 958-973. https://doi.org/10.1016/j.renene.2019.05.063
Sindhu, S., Nehra, V., & Luthra, S. (2017). Investigation of feasibility study of solar farms deployment using hybrid AHP-TOPSIS analysis: Case study of India. Renewable and Sustainable Energy Reviews, 73, 496-511. https://doi.org/10.1016/j.rser.2017.01.135
Taylor, R. (1990). Interpretation of the correlation coefficient: A basic review. Journal of Diagnostic Medical Sonography, 6(1), 35-39. https://doi.org/10.1177/875647939000600106
Thirugnanasambandam, M., Iniyan, S., & Goic, R. (2010). A review of solar thermal technologies. Renewable and Sustainable Energy Reviews, 14(1), 312-322. https://doi.org/10.1016/J.RSER.2009.07.014
Uyan, M. (2013). GIS-based solar farms site selection using analytic hierarchy process (AHP) in Karapinar region Konya/Turkey. Renewable and Sustainable Energy Reviews, 28, 11-17. https://doi.org/10.1016/j.rser.2013.07.042
Van Haaren, R., & Fthenakis, V. (2011). GIS-based wind farm site selection using spatial multi-criteria analysis (SMCA): Evaluating the case for New York State. Renewable and Sustainable Energy Reviews, 15(7), 3332-3340. https://doi.org/10.1016/J.RSER.2011.04.010
Villacreses, G., Gaona, G., Martínez-Gómez, J., & Jijón, D. J. (2017). Wind farms suitability location using geographical information system (GIS), based on multi-criteria decision making (MCDM) methods: The case of continental Ecuador. Renewable Energy, 109, 275-286. https://doi.org/10.1016/j.renene.2017.03.041
Visser, H., & De Nijs, T. (2006). The map comparison kit. Environmental Modelling and Software, 21(3), 346-358. https://doi.org/10.1016/j.envsoft.2004.11.013
Wang, H., Pan, Y., & Luo, X. (2019). Integration of BIM and GIS in sustainable built environment: A review and bibliometric analysis. Automation in Construction, 103, 41-52. https://doi.org/10.1016/J.AUTCON.2019.03.005
Yushchenko, A., de Bono, A., Chatenoux, B., Patel, M. K., & Ray, N. (2018). GIS-based assessment of photovoltaic (PV) and concentrated solar power (CSP) generation potential in West Africa. Renewable and Sustainable Energy Reviews, 81(Part 2), 2088-2103. https://doi.org/10.1016/j.rser.2017.06.021
Zanakis, S. H., Solomon, A., Wishart, N., & Dublish, S. (1998). Multi-attribute decision making: A simulation comparison of select methods. European Journal of Operational Research, 107(3), 507-529. https://doi.org/10.1016/S0377-2217(97)00147-1
Zar, J. H. (1972). Significance testing of the Spearman rank correlation coefficient. Journal of the American Statistical Association, 67(339), 578-580. https://doi.org/10.1080/01621459.1972.10481251
Zar, J. H. (2005). Spearman rank correlation. In Encyclopedia of Biostatistics. Wiley. https://doi.org/10.1002/0470011815.B2A15150
Zoghi, M., Ehsani, A. H., Sadat, M., Amiri, M. J., & Karimi, S. (2017). Optimization solar site selection by fuzzy logic model and weighted linear combination method in arid and semi-arid region: A case study Isfahan-IRAN. Renewable and Sustainable Energy Reviews, 68(Part 2), 986-996. https://doi.org/10.1016/j.rser.2015.07.014
ISSN 0128-7680
e-ISSN 2231-8526