Forecasting large-scale solar power plant energy production based on Monte Carlo simulations and long-short-term memory - Sheeraz Iqbal; Md Shafiullah; Muhammad Aurangzeb; Irfan Jamil; Abdul Rehman; Asif Islam; Amjad Ali; Salah S. Alharbi (Results in Engineering, 2025)
The broad-scale development of solar power plants makes the most significant contribution to reducing energy shortages in rural areas. This study presents a robust forecasting framework combining Monte Carlo Simulation (MCS) and Long Short-Term Memory (LSTM) models to predict the energy production of large-scale solar power plants. Using real-time data from Quaid-e-Azam Solar Park, the proposed model achieved approximately 14 % higher accuracy compared to traditional forecasting techniques, significantly reducing prediction errors with a Mean Absolute Percentage Error (MAPE) of <4 %. The results demonstrate the model’s effectiveness in capturing seasonal variations and managing uncertainties inherent in solar energy forecasting, thereby offering substantial improvements in renewable energy management and planning. In the first scenario, models have been developed using MCS and LSTM and trained on one year of production data to predict future outcomes. In the second scenario, both developed Monte Carlo simulations; Solar energy, Long-Short-Term Memory, and Quaid-e-Azam Solar Park models were used to compare future energy trends. Designing these models using these approaches gives future energy forecast results in various graphs, and it is proven that MCS and LSTM are more efficient techniques than traditional ones based on their findings. Thus, MCS and LSTM are suitable methodologies for analyzing long-term solar energy forecasts for large-scale solar power projects.