A Hybrid Stacking Method for Short-Term Price Forecasting in Electricity Trading Market

The instability of electrical trading prices presents considerable challenges for forecasting and allocating resources in smart grid systems. This paper proposes an electrical price forecasting approach based on hybrid stacking ensemble learning methods. Although weather conditions significantly impact the distribution of electrical loads and prices, different weather-related historical data, including temperature and humidity, would be utilized as input features for the proposed hybrid stacking model based on different Artificial Intelligence (AI) methods. Different from the conventional load forecasting method, the proposed hybrid stacking method includes multiple AI models including XGBoost, CatBoost, Neural Oblivious Decision Ensemble (NODE), Light Gradient-Boosting Machine (LightGBM), Gated Recurrent Unit (GRU), and Long Short-Term Memory (LSTM), with Lasso regression acting as the meta-learner. Considering distributed weather data inputs and the stacking nature of the proposed method, the model seeks to improve forecasting accuracy and more closely represent actual electrical load and price situations. The performance of the hybrid stacking model was experimented with realistic datasets and evaluated by different time series forecasting metrics, including Mean Absolute Percentage Error (MAPE). Comparisons with single AI models show that the proposed hybrid stacking method improves the performance of electrical load and price forecasting and could be beneficial to the operation and allocation of smart grid systems.