Renewable energy sources have been placed as the key to facilitating to provide source of electricity generation. Solar power is one of the most preferable one among renewable energy sources due to the easy to generate in suitable environment. However, there are concerns with the location of solar power plants installation which causes low efficiency and ineffective use. Hence, determining the location for the usage of solar power sources is critical to mitigating those concerns. In addition, Turkey has been focused on investments on sustainable renewable energy sources and there are few studies which work on cities to reveal potential sources. In this study, GIS and intuitionistic fuzzy set based multi-criteria decision-making method is proposed for determining the most suitable areas for solar energy power plant potential site selection in Erzurum province, Turkey. Firstly, a solar energy power plant potential site selection map is made using a GIS program along with considering ecological risks and ecological criteria. Secondly, 20 districts of Erzurum are investigated in terms of 10 criteria (slope, aspect, solar irradiation, land use, wind speed, air temperature, air pressure, air humidity, land surface temperature and transmission line) using intuitionistic fuzzy sets. In these parameters the first time we looked the land use surface. The land use surface is affected the efficiency of the solar power plant. Finally, the comparisons of two methods are done to check consistency of results obtained. The results indicate that both approaches achieve same areas as the most suitable locations for solar power plants installations for Erzurum province in Turkey. The aim of this work is first to investigate possible locations for solar power plant installation using a mapping method, GIS, and then, Intuitionistic Fuzzy is applied to the problem to obtain optimum areas for solar energy. Also, more accurate results are provided comparing results of two methods, GIS and Intuitionistic Fuzzy. The results expose that 25,065.3 km² for solar power plant suitable for solar power plan installation.

As a result of the increase in the number of smart buildings and advances in technology, energy consumption in buildings has become increasingly important. The estimation of energy consumption in buildings is critical for energy efficiency. Accurate estimation of photovoltaic (PV) solar power plant efficiency is crucial for optimizing the performance of renewable energy applications. In this study, advanced machine learning regression models such as XGBoost, CatBoost, LightGBM, AdaBoost and Histogram-Based Gradient Boosting are used to predict PV efficiency based on ten internal features (Open Circuit Voltage (Voc), Short Circuit Current (Isc), Maximum Power (Pmpp), Solar Irradiation Spread (SIS), Maximum Voltage (Vmpp), Maximum Current (Impp), Fill Factor (FF), Parallel Resistance (Rp), Series Resistance (Rs), and Module Temperature (Tm)) of PV module measurements from the Utrecht University Photovoltaic Outdoor Test Facility. As a result, CatBoost outperformed the others, achieving the lowest prediction error MSE of 0.002 and the highest R² value of 0.90. To interpret the model’s predictions, we applied Explainable Artificial Intelligence techniques, in particular SHAP and LIME, which identify key features affecting efficiency and increase model transparency. The integration of these methods provides valuable insights for PV solar power plant design and optimization.