Hybrid model using deep learning for load forecasting #SmartGrids

This paper introduces a hybrid algorithm combining GRU, TCN, and attention mechanism to enhance short-term power load forecasting and energy management. The algorithm utilizes GRU for capturing long-term dependencies, TCN for extracting temporal features, and attention mechanism for adjusting feature importance dynamically. The model’s framework diagram is provided, along with the implementation process involving data preparation, feature engineering, model construction, and fusion. The algorithm integrates GRU and TCN to capture different aspects of load data, while the attention mechanism prioritizes relevant features for load forecasting. The model is trained, uncertainty is modeled, and forecast errors are analyzed for evaluation. The trained model is used for load forecasting and energy management, with experimental validation and application in real-world scenarios. GRU, TCN, and attention mechanism are detailed, showcasing their roles and equations in the hybrid algorithm. The attention mechanism dynamically allocates weights to different positions in the sequence, enhancing the model’s focus. The ethical approval section states that no studies involving human participants or animals were conducted. Overall, the hybrid algorithm effectively captures spatial and temporal characteristics of power load data for accurate short-term load forecasting and energy management.

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Source link: https://www.nature.com/articles/s41598-024-63262-x