Abstract
The load forecast plays an important role in the secure and economic operations of power systems. Due to the increase of the multivariate heterogeneous equipment in the power system, it is difficult to determine the key characteristics of the sample data for the load prediction. Therefore, a Bi-directional long short-term memory network (BiLSTM) combined with attention mechanism is proposed to improve the accuracy of prediction. BiLSTM can grasp the global trend of sample data, and the attention mechanism can extract the key features of sample data. Specifically, this model firstly uses convolutional neural network (CNN) to extract local features from the load data. Finally, a load sample data collected from a certain region of China is used to train the model, and the test results show that the model has a better prediction accuracy compared with the existing models, e.g., long short-term memory neural network (LSTM) and CNN-LSTM.
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Lu, C., Shao, L., Feng, C., Hu, J. (2023). Short-Term Load Forecasting Model Based on BiLSTM and Attention Mechanism. In: Zeng, P., Zhang, XP., Terzija, V., Ding, Y., Luo, Y. (eds) The 37th Annual Conference on Power System and Automation in Chinese Universities (CUS-EPSA). CUS-EPSA 2022. Lecture Notes in Electrical Engineering, vol 1030. Springer, Singapore. https://doi.org/10.1007/978-981-99-1439-5_64
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DOI: https://doi.org/10.1007/978-981-99-1439-5_64
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