DAE-LSTM neural network-based prediction model for segmental line loss rate of low-voltage power grid

Junhong Lin; Daolu Zhang; Dan Wu; Fu Huang

doi:10.1117/12.3004377

25 September 2023 DAE-LSTM neural network-based prediction model for segmental line loss rate of low-voltage power grid

Junhong Lin, Daolu Zhang, Dan Wu, Fu Huang

Proceedings Volume 12788, Second International Conference on Energy, Power, and Electrical Technology (ICEPET 2023); 127885D (2023) https://doi.org/10.1117/12.3004377
Event: Second International Conference on Energy, Power, and Electrical Technology (ICEPET 2023), 2023, Kuala Lumpur, Malaysia

Abstract

The traditional low-voltage grid segmental line loss rate prediction is more often chosen from the tidal method, but the operational limitations of the method lead to the low prediction accuracy of the method. In this regard, a low-voltage grid segmental loss rate prediction model based on the combination of DAE and LSTM neural network is proposed. Firstly, the DAE is used to encode the input content and extract the main features of the input content, and secondly, the encoded input content is put into the LSTM model for training to obtain the low-voltage segmental line loss rate prediction model. In the experiments, the prediction accuracy of the model is verified. The experimental analysis shows that the model has high prediction accuracy when the proposed model is used for LVR segmented line loss rate prediction

Citation Download Citation

Junhong Lin, Daolu Zhang, Dan Wu, and Fu Huang "DAE-LSTM neural network-based prediction model for segmental line loss rate of low-voltage power grid", Proc. SPIE 12788, Second International Conference on Energy, Power, and Electrical Technology (ICEPET 2023), 127885D (25 September 2023); https://doi.org/10.1117/12.3004377

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