Performance evaluation and efficiency analysis of a solar power plant: The case of Şırnak–Cizre

Abstract This study examines the performance and economic viability of two grid‐connected solar power plants in Cizre, commissioned in 2021, with capacities of 560 and 720 kW. The analyses utilize real operating data from 2022 to 2024 to evaluate key technical indicators such as annual energy production, system efficiency, and capacity factor (CF). For energy production forecasting, classical time series models such as autoregressive integrated moving average (ARIMA) and seasonal autoregressive integrated moving average (SARIMA) were compared with artificial neural networks (ANNs) and long short‐term memory networks (LSTMs) models. Model performance was evaluated using mean square error and coefficient of determination (R 2 ). In contrast, the ARIMA and SARIMA models are found to be more limited in datasets with complex seasonality. The findings reveal that energy production reaches its maximum level during the summer months, depending on regional solar potential. Based on 3‐year averages, CFs were calculated as 26% for the 560 kW power plant and 25.9% for the 720 kW power plant. Economic analysis results show that these systems have high profitability potential with an approximate payback period of 4–5 years. In conclusion, the study reveals that solar energy is a technically reliable and economically viable energy solution in regions with high solar radiation potential, such as Cizre. Furthermore, when comparing prediction models, it was determined that the LSTM model performed better than ANNs, ARIMA, and SARIMA models, with a lower error rate and higher prediction accuracy, especially in modeling nonlinear and seasonally variable energy production data.