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.

减少全球化石燃料使用的重要自然能源是可再生能源 (RE)。太阳能是一种重要且可靠的可再生能源。太阳能光伏发电场的选址是空间规划和可再生能源政策方面的一个关键问题。本研究采用基于地理信息系统（GIS）的多影响因素（MIF）技术来提高识别和描绘太阳能光伏发电场最佳位置的精度。选择 GIS 和 MIF 是因为它们能够整合不同的影响因素，促进空间数据的整体分析。选定的影响因素包括太阳辐射、风速、地表温度（LST）、相对湿度、植被、海拔、土地利用、距道路的欧氏距离和坡向。太阳能光伏电站最佳选址圈定显示，“极低”适宜性场地占研究区域的4.866%，“低”适宜性场地占13.190%，“中等”适宜性场地占31.640%，“良好”适宜性场地覆盖研究区域32.347%，太阳能光伏电站选址“非常好”，涵盖了研究区域的 17.957%。敏感性分析结果表明，太阳辐射、相对湿度和海拔对预测精度影响最大。结果验证表明，利用MIF技术对研究区太阳能光伏电站进行预测的准确率为81.80%。GIS与MIF的集成不仅提高了场地适宜性评价的准确性，而且提供了切实可行的实施策略。这项研究为可再生能源政策制定者、城市规划者和其他利益相关者寻求在各自地区确定和开发太阳能发电厂的最佳位置提供了宝贵的见解。