GIS-based multi-influencing factor (MIF) application for optimal site selection of solar photovoltaic power plant in Nashik, India
The significant natural energy sources for reducing the global usage of fossil fuels are renewable energy (RE) sources. Solar energy is a crucial and reliable RE source. Site selection for solar photovoltaic (PV) farms is a crucial issue in terms of spatial planning and RE policies. This study adopt...
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my.uniten.dspace-362642025-03-03T15:41:44Z GIS-based multi-influencing factor (MIF) application for optimal site selection of solar photovoltaic power plant in Nashik, India Rane N.L. G�nen M.A. Mallick S.K. Rane J. Pande C.B. Giduturi M. Bhutto J.K. Yadav K.K. Tolche A.D. Alreshidi M.A. 57219453239 57190371587 57219839603 58316635400 57193547008 58667878700 57200144286 57202908705 57198446685 58677108000 India Maharashtra Nashik Decision making Fossil fuels Geographic information systems Land use Sensitivity analysis Solar concentrators Solar energy Solar power generation Solar power plants Solar radiation Wind Multi criteria decision-making Multi-criteria decision-making Multi-influence factor Multicriteria decision-making Multicriterion decision makings Optimal site Renewable energies Renewable energy source Solar photovoltaic power plants Study areas alternative energy fossil fuel GIS power plant sensitivity analysis site selection spatial planning Site selection The significant natural energy sources for reducing the global usage of fossil fuels are renewable energy (RE) sources. Solar energy is a crucial and reliable RE source. Site selection for solar photovoltaic (PV) farms is a crucial issue in terms of spatial planning and RE policies. This study adopts a Geographic Information System (GIS)-based Multi-Influencing Factor (MIF) technique to enhance the precision of identifying and delineating optimal locations for solar PV farms. The choice of GIS and MIF is motivated by their ability to integrate diverse influencing factors, facilitating a holistic analysis of spatial data. The selected influencing factors include solar radiation, wind speed, Land Surface Temperature (LST), relative humidity, vegetation, elevation, land use, Euclidean distance from roads, and aspect. The optimal sites of solar PV power plant delineated revealed that ?very low? suitability of site covering 4.866% of the study area, ?low? suitability of site 13.190%, ?moderate? suitability of site 31.640%, ?good? suitability of site 32.347%, and ?very good? suitability of site for solar PV power plant encompassing 17.957% of the study area. The sensitivity analysis results show that the solar radiation, relative humidity, and elevation are the most effective on the accuracy of the prediction. The validation of the results shows the accuracy of solar PV power plant prediction using MIF technique in the study area was 81.80%. The integration of GIS and MIF not only enhances the accuracy of site suitability assessment but also provides a practical implementation strategy. This research offers valuable insights for renewable energy policymakers, urban planners, and other stakeholders seeking to identify and develop optimal locations for solar energy power farms in their respective regions. ? 2024, The Author(s). Final 2025-03-03T07:41:44Z 2025-03-03T07:41:44Z 2024 Article 10.1186/s12302-023-00832-2 2-s2.0-85181521962 https://www.scopus.com/inward/record.uri?eid=2-s2.0-85181521962&doi=10.1186%2fs12302-023-00832-2&partnerID=40&md5=bb80bc64d6a7d3a29fb34b021beb3873 https://irepository.uniten.edu.my/handle/123456789/36264 36 1 5 Springer Scopus |
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India Maharashtra Nashik Decision making Fossil fuels Geographic information systems Land use Sensitivity analysis Solar concentrators Solar energy Solar power generation Solar power plants Solar radiation Wind Multi criteria decision-making Multi-criteria decision-making Multi-influence factor Multicriteria decision-making Multicriterion decision makings Optimal site Renewable energies Renewable energy source Solar photovoltaic power plants Study areas alternative energy fossil fuel GIS power plant sensitivity analysis site selection spatial planning Site selection |
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India Maharashtra Nashik Decision making Fossil fuels Geographic information systems Land use Sensitivity analysis Solar concentrators Solar energy Solar power generation Solar power plants Solar radiation Wind Multi criteria decision-making Multi-criteria decision-making Multi-influence factor Multicriteria decision-making Multicriterion decision makings Optimal site Renewable energies Renewable energy source Solar photovoltaic power plants Study areas alternative energy fossil fuel GIS power plant sensitivity analysis site selection spatial planning Site selection Rane N.L. G�nen M.A. Mallick S.K. Rane J. Pande C.B. Giduturi M. Bhutto J.K. Yadav K.K. Tolche A.D. Alreshidi M.A. GIS-based multi-influencing factor (MIF) application for optimal site selection of solar photovoltaic power plant in Nashik, India |
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The significant natural energy sources for reducing the global usage of fossil fuels are renewable energy (RE) sources. Solar energy is a crucial and reliable RE source. Site selection for solar photovoltaic (PV) farms is a crucial issue in terms of spatial planning and RE policies. This study adopts a Geographic Information System (GIS)-based Multi-Influencing Factor (MIF) technique to enhance the precision of identifying and delineating optimal locations for solar PV farms. The choice of GIS and MIF is motivated by their ability to integrate diverse influencing factors, facilitating a holistic analysis of spatial data. The selected influencing factors include solar radiation, wind speed, Land Surface Temperature (LST), relative humidity, vegetation, elevation, land use, Euclidean distance from roads, and aspect. The optimal sites of solar PV power plant delineated revealed that ?very low? suitability of site covering 4.866% of the study area, ?low? suitability of site 13.190%, ?moderate? suitability of site 31.640%, ?good? suitability of site 32.347%, and ?very good? suitability of site for solar PV power plant encompassing 17.957% of the study area. The sensitivity analysis results show that the solar radiation, relative humidity, and elevation are the most effective on the accuracy of the prediction. The validation of the results shows the accuracy of solar PV power plant prediction using MIF technique in the study area was 81.80%. The integration of GIS and MIF not only enhances the accuracy of site suitability assessment but also provides a practical implementation strategy. This research offers valuable insights for renewable energy policymakers, urban planners, and other stakeholders seeking to identify and develop optimal locations for solar energy power farms in their respective regions. ? 2024, The Author(s). |
| author2 |
57219453239 |
| author_facet |
57219453239 Rane N.L. G�nen M.A. Mallick S.K. Rane J. Pande C.B. Giduturi M. Bhutto J.K. Yadav K.K. Tolche A.D. Alreshidi M.A. |
| format |
Article |
| author |
Rane N.L. G�nen M.A. Mallick S.K. Rane J. Pande C.B. Giduturi M. Bhutto J.K. Yadav K.K. Tolche A.D. Alreshidi M.A. |
| author_sort |
Rane N.L. |
| title |
GIS-based multi-influencing factor (MIF) application for optimal site selection of solar photovoltaic power plant in Nashik, India |
| title_short |
GIS-based multi-influencing factor (MIF) application for optimal site selection of solar photovoltaic power plant in Nashik, India |
| title_full |
GIS-based multi-influencing factor (MIF) application for optimal site selection of solar photovoltaic power plant in Nashik, India |
| title_fullStr |
GIS-based multi-influencing factor (MIF) application for optimal site selection of solar photovoltaic power plant in Nashik, India |
| title_full_unstemmed |
GIS-based multi-influencing factor (MIF) application for optimal site selection of solar photovoltaic power plant in Nashik, India |
| title_sort |
gis-based multi-influencing factor (mif) application for optimal site selection of solar photovoltaic power plant in nashik, india |
| publisher |
Springer |
| publishDate |
2025 |
| _version_ |
1825816267377868800 |
| score |
13.244109 |