基于GSA-AGRU的挤压机能耗预测

Energy Consumption Prediction of Extruder Based on GSA-AGRU

在铝型材的生产过程中,挤压机是核心的生产机器,其能耗占铝型材生产能耗的60%以上。针对当前挤压机能耗预测精度低和预测速度慢的问题,提出基于引力搜索优化的注意力机制门控循环单位网络模型(GSA-AGRU)用于预测挤压机的能耗,首先构建注意力机制的门控循环单位网络模型(AGRU),然后加入引力搜索算法(GSA)优化该网络的权重,最后得到最优的GSA-AGRU预测模型。利用某铝型材企业的挤压机生产能耗数据进行实验,结果表明GSA-AGRU模型相比于传统的GRU、LSTM、BP和AGRU模型具有更高的预测精度和更快的预测速度。

In the process of aluminum profile production, the extruder is the core production machine, its energy consumption accounts for more than 60% of the aluminum profile production energy consumption. In view of the current low extrusion machine energy consumption prediction precision and slow speed of prediction problem. The concentration mechanism gating cycle unit network model(GSA-AGRU) based on gravity search optimization was proposed to predict the energy consumption of extruder. Firstly, the attention mechanism gated cycle unit network model(AGRU) was constructed, and then the gravity search algorithm(GSA) was added to optimize the weight of the network. Finally,the optimal gsa-agru prediction model was obtained. The experiment was carried out by using the production energy consumption data of an aluminum profile enterprise, the results show that the GSA-AGRU model has higher prediction accuracy and faster prediction speed than the traditional GRU, LSTM, BP and AGRU models.