# Solar Power System Planning and Design
## 5 Performance Assessment  
[PERSON] and [PERSON] [11] performed a numerical analysis of a photovoltaic-thermal (PV/T) unit with SiO\({}_{2}\)-water nanofluid. The coupled heat conduction equations for the layers and convective heat transfer equations for the channel of the module were solved using the finite volume method. The effects of various particle shapes, solid volume fractions, water inlet temperature, solar irradiation, and wind speed on the thermal and PV efficiency of the unit were analyzed. The performance characteristics of the solar PV-thermal unit determined by the radial basis function artificial neural network were found to be in excellent agreement with the results obtained from computational fluid dynamics modeling.  
[PERSON] et al. [12] carried out a comparative analysis of energy production by a grid-connected experimental PV system composed of various technology modules, which operates in the temperate-climate meteorological conditions of eastern Poland, for the year 2015. The study revealed that the copper indium gallium diselenide (CIGS) technology demonstrated the highest energy production and performance ratio, as well as the lowest observed temperature-related losses. These results can be useful for the prediction of electric energy production by different PV technologies at high latitudes under temperate climate conditions.  
[PERSON] et al. [13] determined the performance and viability of direct normal irradiation for three solar tower power plants to be installed in the Algerian highlands and the Sahara (Bechar, El Oued, and Djelfa regions). Each plant, with the annual production specification of 20 MW, is equipped with a supply of molten salt, an external receiver, and a field of heliostats. Results showed that there is a strong and direct relationship between the solar multiple, power generation, and storage capacity hours.  
Machine learning methods were successfully applied in PV output prediction models. [PERSON] et al. [14] proposed a hybrid short-term forecasting method based on the variational mode decomposition (VMD) technique, the deep belief network (DBN), and the auto-regressive moving average (ARMA) model to improve forecasting accuracy. The results showed that the hybrid forecasting method offers better accuracy and stability than the single prediction methods. Additionally, [PERSON] et al. [15] developed an ultrashort-term forecasting model based on the phase space reconstruction and deep neural network (DNN) by considering the characteristics of the net load. The performance of this model was verified using real data, with superior accuracy in forecasting the net load under high PV penetration rates and different weather conditions.  
Solar potential assessment using GIS can be placed in three different categories: (1) physical potential, which is the total amount of solar energy reaching a target surface or the total solar radiation on a surface or rooftop; (2) geographic potential, which is the spatial availability of a surface or building rooftop where solar energy can be obtained; and (3) technical potential, which represents the total amount of electricity considering the technical characteristics of the PV system. [PERSON] et al. [3] reviewed 39 published articles on GIS-based solar potential assessment.