Short-term power load forecast using OOA optimized bidirectional long short-term memory network with spectral attention for the frequency domain
Jingrui Liu, Zhiwen Hou, Tianxiang Yin
Abstract
Accurate short-term power load forecasting is crucial for maintaining the stability and efficiency of modern power systems, especially in the face of increasing volatility and complexity. This paper introduces a novel approach for short-term power load forecasting by integrating the Osprey Optimization Algorithm (OOA) with a Bidirectional Long Short-Term Memory (BiLSTM) network enhanced by a spectral attention mechanism. The OOA optimizes the hyperparameters of the BiLSTM, improving the model's global search capability and avoiding local optima. The spectral attention mechanism further refines the model by focusing on key frequency components in the time series data. Experimental results demonstrate the superior performance of the proposed model, achieving an RMSE of 0.1382, MAE of 0.0659, and MAPE of 1.14 %, significantly outperforming traditional methods. The model's effectiveness is particularly notable in capturing complex patterns during critical peak and trough periods, making it a valuable tool for enhancing grid operation and management.