Identification of Abnormal Electricity Consumption Behavior Based on Bi-LSTM Recurrent Neural Network

Fang, Zhou; Huang, Yuteng; Chen, Xiaoxiao; Gong, Kangjia; Zhou, Houpan

doi:10.1109/icpre48497.2019.9034719

Cited by 5 publications

(1 citation statement)

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“…As an improved version of LSTM, the Bidirectional LSTM (Bi-LSTM) has better performance via adding a reverse-calculation module. Hence, a Bi-LSTM-based PGPM, which is used to predict the abnormal electricity consumption in power grids, was proposed in [19]. In the Bi-LSTM-based PGPM, the framework of Tensorflow was used to achieve feature extraction and power generation prediction.…”

Section: Introductionmentioning

confidence: 99%

An Improved Generating Energy Prediction Method Based on Bi-LSTM and Attention Mechanism

Zhang

et al. 2022

Electronics

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The energy generated by a photovoltaic power station is affected by environmental factors, and the prediction of the generating energy would be helpful for power grid scheduling. Recently, many power generation prediction models (PGPM) based on machine learning have been proposed, but few existing methods use the attention mechanism to improve the prediction accuracy of generating energy. In the paper, a PGPM based on the Bi-LSTM model and attention mechanism was proposed. Firstly, the environmental factors with respect to the generating energy were selected through the Pearson coefficient, and then the principle and implementation of the proposed PGPM were detailed. Finally, the performance of the proposed PGPM was evaluated through an actual data set collected from a photovoltaic power station in Suzhou, China. The experimental results showed that the prediction error of proposed PGPM was only 8.6 kWh, and the fitting accuracy was more than 0.99, which is better than existing methods.

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Section: Introductionmentioning

confidence: 99%