Abstract
Power load forecasting plays an important role in the development of power system, which can provide crucial guidance for power supply. The short term power load forecasting can ensure the safety and stability of power grids in a short time. To solve the problem of insufficient accuracy of prediction, Attention-Bidirectional LSTM based short term power load forecasting is proposed in this paper. This model remains the basic LSTM structure, and using attention mechanism to enhance some pivotal weight. Since the multivariate influents the training differently, attention mechanism is included to strengthen the impact of key features. The simulation results show that the Attention-Bidirectional LSTM outperformers Bidirectional LSTM based on different evaluation metrics.
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