Abstract
This article presents an adaptive mathematical model designed for accurate energy production forecasting in photovoltaic power plants. The research object includes climatic parameters such as solar radiation (300–800 W/m²), temperature, humidity, and wind speed. The primary issue addressed is enhancing forecasting accuracy under variable weather conditions and ensuring the adaptability of the model. As a result, a hybrid neural network model based on LSTM and iTransformer was developed. The model improved forecasting accuracy up to 93.1%, achieving MAE = 1.75 and MSE = 3.25. When applying the transfer learning method, the MAE was reduced by up to 30% (e.g., in the “dry” scenario – from 3.1 to 2.2). Computation time was reduced from 12.5 seconds to 4.3 seconds (–65.6%) using a GPU (RTX 3060). The main advantage of the model lies in its ability to accurately account for complex seasonal and temporal dependencies, showing higher accuracy than traditional methods (70–90%). This solution can be effectively applied in real-time forecasting systems, grid load management, and solar energy storage strategies.