Equipment consumption prediction based on GBO-LSTM-Attention

Equipment consumption prediction is the premise and foundation of good technical support work, subject to a variety of factors affecting more, put forward a long and short-term memory network (LSTM) and Attention mechanism combination of equipment consumption prediction method, play the LSTM and Attention in the prediction of the advantages of the LSTM and Attention, the use of gradient optimization algorithm on the LSTM- Attention's parameters beach_size, learning rate, regularization parameter, and the number of LSTM hidden layer units, etc. The model is tested, and the coefficient of determination is 98.55%, the evaluation indexes is substantially improved, which proves that the model has higher prediction accuracy and is of great significance for equipment protection.