Artificial Intelligence and Optimization Based Output Power Forecasting in Power Plants

The demand for energy has been rapidly increasing on a global scale due to industrialization and population growth. This increase necessitates making energy production processes more efficient, sustainable, and predictable. Therefore, forecasting models based on artificial intelligence and heuristic optimization techniques have become a crucial component of decision support systems in the energy sector. In this study, a forecasting model based on Particle Swarm Optimization (PSO) was developed, and the hyperparameters of the Long Short-Term Memory (LSTM) model used for forecasting were optimized using PSO. During the training and testing stages, a dataset consisting of operational data from a power plant was utilized. The model's performance was evaluated using statistical error metrics such as the coefficient of determination (R²), root mean square error (RMSE), mean squared error (MSE), and mean absolute error (MAE). The results demonstrate that the proposed PSO-based optimization approach provides high accuracy in energy production forecasting and offers a significant alternative to traditional methods.