Forecasting large-scale solar power plant energy production based on Monte Carlo simulations and long-short-term memory

• Enhanced LSTM optimization : We integrate multiple optimization algorithms into the LSTM training process, significantly improving prediction accuracy over conventional single-optimizer approaches. • Data uncertainty management: Our methodology incorporates robust uncertainty management techniques to address the variability in solar data caused by weather conditions and measurement inconsistencies. • Novel feature engineering : We propose new techniques for feature extraction, specifically designed for solar forecasting, allowing the model to capture complex dependencies between solar irradiance, weather patterns, and energy output. • Hybrid modeling architecture: We present a hybrid framework that combines LSTM capabilities with other modeling strengths, improving long-term dependency handling and generalization performance. • Comprehensive dataset and analysis: Utilizing a one-year real-time dataset from QASP, we provide a detailed performance comparison of MCS and LSTM approaches through graphical representation and quantitative metrics. 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 less than 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.