LSTM hub single product energy consumption prediction based on incremental clustering

Abstract

The invention discloses LSTM hub single product energy consumption prediction based on incremental clustering, and relates to the technical field of hub single product energy consumption prediction. According to the method, clustering analysis of the hub characteristic parameters is achieved through dynamic incremental density clustering based on PCA, the type of a historical product to which a newly-added product belongs is obtained, and a Pearson coefficient and Adaptive-Lasso algorithm are utilized on the basis of an energy consumption influence factor system for screening out single product energy consumption high interpretability factors; by means of BP, prediction of new product strong interpretability variables is achieved, an LSTM energy consumption prediction model is constructedfor each cluster product, effective prediction of new product unit consumption is achieved, optimization of LSTM is achieved through ADE, and meanwhile an incremental learning strategy is introduced to achieve dynamic updating of the model. The effectiveness of the prediction method is verified, the root-mean-square error RMSE of energy consumption prediction is lowered to 0.016524, compared withother algorithms without incremental learning, the root-mean-square error RMSE is lowered by 0.013653 on average, meanwhile, the ADE search performance of the algorithm is good, and the RMSE of a training set is reduced by 0.004089 compared with that of the average of DE-LSTM with incremental learning , and the operation time is effectively shortened.

Description

technical field [0001] The invention relates to the technical field of energy consumption prediction of a single wheel hub product, in particular to an incremental clustering-based LSTM single product energy consumption prediction method for a wheel hub. Background technique [0002] The strong demand in the vehicle market drives the domestic wheel industry to achieve rapid development. At present, there are more than 300 wheel manufacturers in my country, and the output of automobile wheels maintains a double-digit growth rate. It is estimated that in 2022, the demand for wheels in the domestic automotive aftermarket will be 604. About 10,000 pieces, it can be seen that wheel hub products have entered the "Made in China Era". Therefore, in order to respond to the needs of the market and meet the personalized customization requirements of customers, wheel manufacturing enterprises have gradually developed into a multi-category and small-batch production mode. However, when n...

AI Technical Summary

Problems solved by technology

Since the relationship between energy consumption and its influencing factors is nonlinear, the use of neural networks can achieve more accurate predictions, but the time modeling ability of traditional feedforward neural networks is quite limited, and the predicted value of the output depends on the input feature sequence for a long time. Historically, LSTMs have been able to solve the problem of learning long-term dependencies

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