Sustainable solar energy site suitability using explainable Generative Artificial Intelligence (GenXAI) enhanced MCDM
Abstract
Sustainable suitability analysis of a site is vital for the sustainable development of solar energy, and it should take into account several environmental and infrastructural factors. Classical GIS-based Multi-Criteria Decision-Making (MCDM) methods, like the MIF method combined with TOPSIS, usually require subjective weights manually set by decision-makers on each criterion that may turn out to be highly biased when intuitively given. In this study, a novel method for the solar farm siting based on explainable Generative Artificial Intelligence (GenXAI) model has been adopted to get the objective optimal weights of MIF criteria. A GIS-based approach along with TOPSIS is utilized in combining 12 climatic, geophysical, and accessibility indicating factors to produce a holistic map of sustainable solar suitability. The interpretability layer of GenXAI increases transparency to the decision process and approximates factors contributions more accurately. Validation of the suitability map with known suitable sites using the Area Under the ROC Curve (AUC) shows that GenXAI -based weighting outperforms manual weighing, even when allowing expert feedback on optimal combination. The AUC of the model, based on traditional MIF weights in, improved from 0.839 to 0.847 and became an even better predictor with weights determined by GenXAI. The results indicate that the interpretability of the generative AI could be applied to MCDM, enhancing the objectivity and accuracy of solar site suitability evaluation, thereby providing a more reliable tool for sustainable energy planning.
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References
[1] Creutzig F, Agoston P, Goldschmidt JC, Luderer G, Nemet G, Pietzcker RC. The underestimated potential of solar energy to mitigate climate change. Nature Energy. 2017 Aug 25;2(9):1-9. https://doi.org/10.1038/nenergy.2017.140
[2] Hsueh SL, Feng Y, Sun Y, Jia R, Yan MR. Using AI-MCDM model to boost sustainable energy system development: A case study on solar energy and rainwater collection in guangdong province. Sustainability. 2021 Nov 12;13(22):12505. https://doi.org/10.3390/su132212505
[3] Ghasempour R, Nazari MA, Ebrahimi M, Ahmadi MH, Hadiyanto H. Multi-Criteria Decision Making (MCDM) Approach for Selecting Solar Plants Site and Technology: A Review. International Journal of Renewable Energy Development. 2019 Feb 1;8(1). https://doi.org/10.14710/ijred.8.1.15-25
[4] Jahangiri M, Shamsabadi AA, Mostafaeipour A, Rezaei M, Yousefi Y, Pomares LM. Using fuzzy MCDM technique to find the best location in Qatar for exploiting wind and solar energy to generate hydrogen and electricity. International Journal of Hydrogen Energy. 2020 May 18;45(27):13862-75. https://doi.org/10.1016/j.ijhydene.2020.03.101
[5] Majumder M, Saha AK. Feasibility model of solar energy plants by ANN and MCDM techniques. Springer Singapore; 2016 Apr 29. https://doi.org/10.1007/978-981-287-308-8
[6] Çolak M, Kaya İ. Prioritization of renewable energy alternatives by using an integrated fuzzy MCDM model: A real case application for Turkey. Renewable and sustainable energy reviews. 2017 Dec 1;80:840-53. https://doi.org/10.1016/j.rser.2017.05.194
[7] Wang CN, Nguyen VT, Thai HT, Duong DH. Multi-criteria decision making (MCDM) approaches for solar power plant location selection in Viet Nam. Energies. 2018 Jun 8;11(6):1504. https://doi.org/10.3390/en11061504
[8] Lak Kamari M, Isvand H, Alhuyi Nazari M. Applications of multi-criteria decision-making (MCDM) methods in renewable energy development: A review. Renewable Energy Research and Applications. 2020 Jan 1;1(1):47-54.
[9] Wang CN, Chung YC, Wibowo FD, Dang TT, Nguyen NA. Site selection of solar power plants using hybrid MCDM models: a case study in Indonesia. Energies. 2023 May 11;16(10):4042. https://doi.org/10.3390/en16104042
[10] Almasad A, Pavlak G, Alquthami T, Kumara S. Site suitability analysis for implementing solar PV power plants using GIS and fuzzy MCDM based approach. Solar Energy. 2023 Jan 1;249:642-50. https://doi.org/10.1016/j.solener.2022.11.046
[11] Azhar NA, Radzi NA, Wan Ahmad WS. Multi-criteria decision making: a systematic review. Recent Advances in Electrical & Electronic Engineering (Formerly Recent Patents on Electrical & Electronic Engineering). 2021 Dec 1;14(8):779-801. https://doi.org/10.2174/2352096514666211029112443
[12] Taylan O, Alamoudi R, Kabli M, AlJifri A, Ramzi F, Herrera-Viedma E. Assessment of energy systems using extended fuzzy AHP, fuzzy VIKOR, and TOPSIS approaches to manage non-cooperative opinions. Sustainability. 2020 Mar 31;12(7):2745. https://doi.org/10.3390/su12072745
[13] Sahabuddin M, Khan I. Multi-criteria decision analysis methods for energy sector's sustainability assessment: Robustness analysis through criteria weight change. Sustainable Energy Technologies and Assessments. 2021 Oct 1;47:101380. https://doi.org/10.1016/j.seta.2021.101380
[14] Kaya T, Kahraman C. Multicriteria renewable energy planning using an integrated fuzzy VIKOR & AHP methodology: The case of Istanbul. Energy. 2010 Jun 1;35(6):2517-27. https://doi.org/10.1016/j.energy.2010.02.051
[15] San Cristóbal JR. Multi-criteria decision-making in the selection of a renewable energy project in spain: The Vikor method. Renewable energy. 2011 Feb 1;36(2):498-502. https://doi.org/10.1016/j.renene.2010.07.031
[16] Mardani A, Jusoh A, Zavadskas EK, Cavallaro F, Khalifah Z. Sustainable and renewable energy: An overview of the application of multiple criteria decision making techniques and approaches. Sustainability. 2015 Oct 19;7(10):13947-84. https://doi.org/10.3390/su71013947
[17] Hsueh SL, Feng Y, Sun Y, Jia R, Yan MR. Using AI-MCDM model to boost sustainable energy system development: A case study on solar energy and rainwater collection in guangdong province. Sustainability. 2021 Nov 12;13(22):12505. https://doi.org/10.3390/su132212505
[18] Hai T, Basem A, Alizadeh AA, Sharma K, Jasim DJ, Rajab H, Mabrouk A, Kolsi L, Rajhi W, Maleki H, Sawaran Singh NS. Integrating artificial neural networks, multi-objective metaheuristic optimization, and multi-criteria decision-making for improving MXene-based ionanofluids applicable in PV/T solar systems. Scientific Reports. 2024 Nov 27;14(1):29524. https://doi.org/10.1038/s41598-024-81044-3
[19] He Q, Zheng H, Ma X, Wang L, Kong H, Zhu Z. Artificial intelligence application in a renewable energy-driven desalination system: A critical review. Energy and AI. 2022 Jan 1;7:100123. https://doi.org/10.1016/j.egyai.2021.100123
[20] Nguyen HN, Tran QT, Ngo CT, Nguyen DD, Tran VQ. Solar energy prediction through machine learning models: A comparative analysis of regressor algorithms. PloS one. 2025 Jan 2;20(1):e0315955. https://doi.org/10.1371/journal.pone.0315955
[21] Ozdemir G, Kuzlu M, Catak FO. Machine learning insights into forecasting solar power generation with explainable AI. Electrical Engineering. 2025 Jun;107(6):7329-50. https://doi.org/10.1007/s00202-024-02933-4
[22] Suanpang P, Jamjuntr P. Machine learning models for solar power generation forecasting in microgrid application implications for smart cities. Sustainability. 2024 Jul 17;16(14):6087. https://doi.org/10.3390/su16146087
[23] Levent İ, Şahin G, Işık G, van Sark WG. Comparative analysis of advanced machine learning regression models with advanced artificial intelligence techniques to predict rooftop PV solar power plant efficiency using indoor solar panel parameters. Applied Sciences. 2025 Mar 18;15(6):3320. https://doi.org/10.3390/app15063320
[24] Bakht MP, Mohd MN, Ibrahim BS, Khan N, Sheikh UU, Ab Rahman AA. Advanced automated machine learning framework for photovoltaic power output prediction using environmental parameters and SHAP interpretability. Results in Engineering. 2025 Mar 1;25:103838. https://doi.org/10.1016/j.rineng.2024.103838
[25] Li Y, Zhou W, Wang Y, Miao S, Yao W, Gao W. Interpretable deep learning framework for hourly solar radiation forecasting based on decomposing multi-scale variations. Applied Energy. 2025 Jan 1;377:124409. https://doi.org/10.1016/j.apenergy.2024.124409
[26] Amer M, Sajjad U, Hamid K, Rubab N. Reliable prediction of solar photovoltaic power and module efficiency using Bayesian surrogate assisted explainable data-driven model. Results in Engineering. 2024 Dec 1;24:103226. https://doi.org/10.1016/j.rineng.2024.103226
[27] Nallakaruppan MK, Shankar N, Bhuvanagiri PB, Padmanaban S, Khan SB. Advancing solar energy integration: Unveiling XAI insights for enhanced power system management and sustainable future. Ain Shams Engineering Journal. 2024 Jun 1;15(6):102740. https://doi.org/10.1016/j.asej.2024.102740
[28] Liu J, Chen Y, Chen J, Li T. Research on collaborative decision-making model for site selection of wind and solar power stations based on AI large model and GAN. Microchemical Journal. 2025 Sep 18:115406. https://doi.org/10.1016/j.microc.2025.115406
[29] Janke JR. Multicriteria GIS modeling of wind and solar farms in Colorado. Renewable Energy. 2010 Oct 1;35(10):2228-34. https://doi.org/10.1016/j.renene.2010.03.014
[30] Charabi Y, Gastli A. PV site suitability analysis using GIS-based spatial fuzzy multi-criteria evaluation. Renewable Energy. 2011 Sep 1;36(9):2554-61. https://doi.org/10.1016/j.renene.2010.10.037
[31] Sánchez-Lozano JM, Antunes CH, García-Cascales MS, Dias LC. GIS-based photovoltaic solar farms site selection using ELECTRE-TRI: Evaluating the case for Torre Pacheco, Murcia, Southeast of Spain. Renewable Energy. 2014 Jun 1;66:478-94. https://doi.org/10.1016/j.renene.2013.12.038
[32] Watson JJ, Hudson MD. Regional Scale wind farm and solar farm suitability assessment using GIS-assisted multi-criteria evaluation. Landscape and urban planning. 2015 Jun 1;138:20-31. https://doi.org/10.1016/j.landurbplan.2015.02.001
[33] Al Garni HZ, Awasthi A. Solar PV power plant site selection using a GIS-AHP based approach with application in Saudi Arabia. Applied energy. 2017 Nov 15;206:1225-40. https://doi.org/10.1016/j.apenergy.2017.10.024
[34] Akkas OP, Erten MY, Cam E, Inanc N. Optimal site selection for a solar power plant in the Central Anatolian Region of Turkey. International Journal of Photoenergy. 2017;2017(1):7452715. https://doi.org/10.1155/2017/7452715
[35] Wu Y, Zhang B, Wu C, Zhang T, Liu F. Optimal site selection for parabolic trough concentrating solar power plant using extended PROMETHEE method: A case in China. Renewable Energy. 2019 Dec 1;143:1910-27. https://doi.org/10.1016/j.renene.2019.05.131
[36] Noorollahi E, Fadai D, Akbarpour Shirazi M, Ghodsipour SH. Land suitability analysis for solar farms exploitation using GIS and fuzzy analytic hierarchy process (FAHP)-a case study of Iran. Energies. 2016 Aug 19;9(8):643. https://doi.org/10.3390/en9080643
[37] Heidary Dahooie J, Husseinzadeh Kashan A, Shoaei Naeini Z, Vanaki AS, Zavadskas EK, Turskis Z. A hybrid multi-criteria-decision-making aggregation method and geographic information system for selecting optimal solar power plants in Iran. Energies. 2022 Apr 11;15(8):2801. https://doi.org/10.3390/en15082801
[38] Jbaihi O, Ouchani FZ, Merrouni AA, Cherkaoui M, Ghennioui A, Maaroufi M. An AHP-GIS based site suitability analysis for integrating large-scale hybrid CSP+ PV plants in Morocco: An approach to address the intermittency of solar energy. Journal of Cleaner Production. 2022 Oct 1;369:133250. https://doi.org/10.1016/j.jclepro.2022.133250
[39] Wang CN, Dang TT, Bayer J. A two-stage multiple criteria decision making for site selection of solar photovoltaic (PV) power plant: A case study in Taiwan. IEEE Access. 2021 May 19;9:75509-25. https://doi.org/10.1109/ACCESS.2021.3081995
[40] Wang CN, Dang TT, Wang JW. A combined Data Envelopment Analysis (DEA) and Grey Based Multiple Criteria Decision Making (G-MCDM) for solar PV power plants site selection: A case study in Vietnam. Energy Reports. 2022 Nov 1;8:1124-42. https://doi.org/10.1016/j.egyr.2021.12.045
[41] Şahin G, Koç A, van Sark W. Multi-criteria decision making for solar power-Wind power plant site selection using a GIS-intuitionistic fuzzy-based approach with an application in the Netherlands. Energy Strategy Reviews. 2024 Jan 1;51:101307. https://doi.org/10.1016/j.esr.2024.101307
[42] Yilmaz İ, Kocer A, Aksoy E. Site selection for solar power plants using GIS and fuzzy analytic hierarchy process: Case study of the western mediterranean region of Turkiye. Renewable Energy. 2024 Dec 1;237:121799. https://doi.org/10.1016/j.renene.2024.121799
[43] Sharma K, Tiwari R, Wadhwani AK, Chaturvedi S. Analyzing the Spatiotemporal Urban Growth Dynamics in Nashik, India from 1992 to 2042 Using MLC and MLP-MCA Algorithms. Journal of the Indian Society of Remote Sensing. 2025 May 27:1-9. https://doi.org/10.1007/s12524-025-02209-9
[44] Sharma K, Tiwari R, Wadhwani AK, Chaturvedi S. Evaluating the impact of land use land cover changes on urban ecosystem services in Nashik, India: a RS-GIS based approach. Environmental Earth Sciences. 2024 Dec;83(24):683. https://doi.org/10.1007/s12665-024-11965-9
[45] Sharma K, Tiwari R, Wadhwani AK, Chaturvedi S. Spatiotemporal analysis of land surface temperature trends in Nashik, India: A 30-year study from 1992 to 2022. Earth Science Informatics. 2024 Jun;17(3):2107-28. https://doi.org/10.1007/s12145-024-01260-3
[46] Rane NL, Günen MA, Mallick SK, Rane J, Pande CB, Giduturi M, Bhutto JK, Yadav KK, Tolche AD, Alreshidi MA. GIS-based multi-influencing factor (MIF) application for optimal site selection of solar photovoltaic power plant in Nashik, India. Environmental Sciences Europe. 2024 Jan 6;36(1):5. https://doi.org/10.1186/s12302-023-00832-2
[47] Yeh TM, Pai FY, Liao CW. Using a hybrid MCDM methodology to identify critical factors in new product development. Neural Computing and Applications. 2014 Mar;24(3):957-71. https://doi.org/10.1007/s00521-012-1314-6
[48] Shahnazari A, Rafiee M, Rohani A, Nagar BB, Ebrahiminik MA, Aghkhani MH. Identification of effective factors to select energy recovery technologies from municipal solid waste using multi-criteria decision making (MCDM): A review of thermochemical technologies. Sustainable energy technologies and assessments. 2020 Aug 1;40:100737. https://doi.org/10.1016/j.seta.2020.100737
[49] Tzeng GH, Chiang CH, Li CW. Evaluating intertwined effects in e-learning programs: A novel hybrid MCDM model based on factor analysis and DEMATEL. Expert systems with Applications. 2007 May 1;32(4):1028-44. https://doi.org/10.1016/j.eswa.2006.02.004
[50] Dong J, Liu D, Wang D, Zhang Q. Identification of key influencing factors of sustainable development for traditional power generation groups in a market by applying an extended MCDM model. Sustainability. 2019 Mar 22;11(6):1754. https://doi.org/10.3390/su11061754
[51] Saraswat SK, Digalwar AK. Evaluation of energy sources based on sustainability factors using integrated fuzzy MCDM approach. International Journal of Energy Sector Management. 2021 Jan 22;15(1):246-66. https://doi.org/10.1108/IJESM-07-2020-0001
[52] Pineda PJ, Liou JJ, Hsu CC, Chuang YC. An integrated MCDM model for improving airline operational and financial performance. Journal of Air Transport Management. 2018 May 1;68:103-17. https://doi.org/10.1016/j.jairtraman.2017.06.003
[53] Sahoo SK, Goswami SS. A comprehensive review of multiple criteria decision-making (MCDM) methods: advancements, applications, and future directions. Decision Making Advances. 2023 Jun 28;1(1):25-48. https://doi.org/10.31181/dma1120237
[54] Kaya İ, Çolak M, Terzi F. Use of MCDM techniques for energy policy and decision‐making problems: A review. International Journal of Energy Research. 2018 Jun 10;42(7):2344-72. https://doi.org/10.1002/er.4016
[55] Stojčić M, Zavadskas EK, Pamučar D, Stević Ž, Mardani A. Application of MCDM methods in sustainability engineering: A literature review 2008-2018. Symmetry. 2019 Mar 8;11(3):350. https://doi.org/10.3390/sym11030350
[56] Muhsen YR, Husin NA, Zolkepli MB, Manshor N. A systematic literature review of fuzzy-weighted zero-inconsistency and fuzzy-decision-by-opinion-score-methods: assessment of the past to inform the future. Journal of Intelligent & Fuzzy Systems. 2023 Aug 24;45(3):4617-38. https://doi.org/10.3233/JIFS-230803
[57] Kahraman C, Onar SC, Oztaysi B. Fuzzy multicriteria decision-making: a literature review. International journal of computational intelligence systems. 2015 Jul 4;8(4):637-66. https://doi.org/10.1080/18756891.2015.1046325
[58] Shinde SP, Barai VN, Gavit BK, Kadam SA, Atre AA, Pande CB, Pal SC, Radwan N, Tolche AD, Elkhrachy I. Assessment of groundwater potential zone mapping for semi-arid environment areas using AHP and MIF techniques. Environmental Sciences Europe. 2024 Dec;36(1):1-20. https://doi.org/10.1186/s12302-024-00906-9
[59] Pande CB, Moharir KN, Panneerselvam B, Singh SK, Elbeltagi A, Pham QB, Varade AM, Rajesh J. Delineation of groundwater potential zones for sustainable development and planning using analytical hierarchy process (AHP), and MIF techniques. Applied Water Science. 2021 Dec;11(12):186. https://doi.org/10.1007/s13201-021-01522-1
[60] Ehsan M, Shabbir H, Al-Quraishi AM, Al-Ansari N, Ahmad Z, Abdelrahman K, Sohail MT, Manzoor Z, Shafi A, Elbeltagi A. Groundwater delineation for sustainable improvement and development aided by GIS, AHP, and MIF techniques. Applied Water Science. 2024 Feb;14(2):23. https://doi.org/10.1007/s13201-023-02065-3
[61] Singh L, Saravanan S, Jennifer JJ, Abijith D. Application of multi-influence factor (MIF) technique for the identification of suitable sites for urban settlement in Tiruchirappalli City, Tamil Nadu, India. Asia-Pacific Journal of Regional Science. 2021 Oct;5(3):797-823. https://doi.org/10.1007/s41685-021-00194-8
[62] Štirbanović Z, Gardić V, Stanujkić D, Marković R, Sokolović J, Stevanović Z. Comparative MCDM analysis for AMD treatment method selection. Water Resources Management. 2021 Sep;35(11):3737-53. https://doi.org/10.1007/s11269-021-02914-3
[63] Choudhury S, Howladar P, Majumder M, Saha AK. Application of novel MCDM for location selection of surface water treatment plant. IEEE Transactions on Engineering Management. 2019 Sep 20;69(5):1865-77. https://doi.org/10.1109/TEM.2019.2938907
[64] Bui HA, Nguyen XT. A novel multicriteria decision-making process for selecting spot welding robot with removal effects of criteria techniques. International Journal on Interactive Design and Manufacturing (IJIDeM). 2024 Mar;18(2):1033-52. https://doi.org/10.1007/s12008-023-01650-9
[65] Dey S, Shukla UK, Mehrishi P, Mall RK. Appraisal of groundwater potentiality of multilayer alluvial aquifers of the Varuna river basin, India, using two concurrent methods of MCDM. Environment, Development and Sustainability. 2021 Dec;23(12):17558-89. https://doi.org/10.1007/s10668-021-01400-5
[66] Parvaneh F, Hammad A. Application of Multi-Criteria Decision-Making (MCDM) to select the most sustainable Power-Generating technology. Sustainability. 2024 Apr 15;16(8):3287. https://doi.org/10.3390/su16083287
[67] Zandi I, Lotfata A. Evaluating solar power plant sites using integrated GIS and MCDM methods: a case study in Kermanshah Province. Scientific Reports. 2025 Jan 26;15(1):3288. https://doi.org/10.1038/s41598-025-87476-9
[68] Zakeri S, Konstantas D, Sorooshian S, Chatterjee P. A novel ML-MCDM-based decision support system for evaluating autonomous vehicle integration scenarios in Geneva's public transportation. Artificial intelligence review. 2024 Sep 30;57(11):310. https://doi.org/10.1007/s10462-024-10917-w