The algorithm of current prediction based on multi-dimensional Long Short Term Memory networks

Abstract

Precisely predict the current on the node circuit in advance, which can facilitate the manager to manage and control the risk in advance. The current, voltage, residual current, cable temperature, ambient temperature, and ambient humidity on the circuit constitute a multi-dimensional sequence characteristic sequence data. This paper proposes an improved Long Short-Term Memory (LSTM) cyclic neural network method, which combines multi-dimensional Train the neural network in a specific way to obtain the LSTM current prediction model. The experiment is validated with the perception data in an intelligent building, and compared with the classic neural network and Recurrent Neural Network (RNN) prediction method. The experimental results show that, compared with the prediction methods such as BP neural network and RNN network, the multi-dimensional LSTM has a higher accuracy in predicting the current in the circuit, and the MSE index is as low as 0.5. And this method can detect potential electrical fire hazards and give early warning 1–2 time intervals in advance to avoid electrical fires.

Keywords

• Electrical fire
• Current prediction
• LSTM
• Neural network
• Multi-dimensional time series