高负载动态工况下工业机器人的能耗预测OA

The power of industrial robots under high-load and highly fluctuating processing conditions showed non-stationary and multi-source coupling characteristics,which led to the problems of reduced ac-curacy and stability of energy consumption prediction models under cross-operation conditions.Multi-source time series data were collected from the self-built processing experimental platform.The heteroge-neous data were synchronized and resampled through timestamps,and power tags were constructed using sliding windows.The prediction results of random forest,gradient boosting tree,support vector regres-sion,multi-layer perceptive machine and two fusion structure models under multiple working conditions were compared.The results show that the energy consumption prediction result of the gradient boosting tree+support vector regression fusion model is the best in the working conditions without participation in training,with an average absolute error of 3.73%.The research reveales the predictive characteristics of dif-ferent models under high-dynamic processing conditions,which may provide technical support for energy effi-ciency modeling,process optimization and green operations of high-load processing of the industrial robots.