Energy Consumption Prediction of a CNC Machining Process With Incomplete Data

Energy consumption prediction of a CNC machining process is important for energy efficiency optimization strategies.To improve the generalization abilities,more and more parameters are acquired for energy prediction modeling.While the data collected from workshops may be incomplete because of misoperation,unstable network connections,and frequent transfers,etc.This work proposes a framework for energy modeling based on incomplete data to address this issue.First,some necessary preliminary operations are used for incomplete data sets.Then,missing values are estimated to generate a new complete data set based on generative adversarial imputation nets(GAIN).Next,the gene expression programming(GEP)algorithm is utilized to train the energy model based on the generated data sets.Finally,we test the predictive accuracy of the obtained model.Computational experiments are designed to investigate the performance of the proposed framework with different rates of missing data.Experimental results demonstrate that even when the missing data rate increases to 30%,the proposed framework can still make efficient predictions,with the corresponding RMSE and MAE 0.903 k J and 0.739 k J,respectively.

Federated learning-based short-term building energy consumption prediction method for solving the data silos problem

Transfer learning is an effective method to predict the energy consumption of information-poor buildings by learning transferable knowledge from operational data of information-rich buildings.However,it is not recommended to directly use the operational data without protection due to the risk of leaking occupants’privacy.To address this problem,this study proposes a federated learning-based method to learn transferable knowledge from building operational data without privacy leaking.It trains a transferable federated model based on the operational data from the buildings similar to the target building with limited data.An advanced secure aggregation algorithm is adopted in the training process to ensure that no one can infer private information from the training data.The federated model obtained is evaluated by comparing it with the standalone model without federated learning based on 13 similar office buildings from the Building Data Genome Project.The results show that the federated model outperforms the standalone model concerning the prediction accuracy and training time.On average,the federated model achieves a 25.4%decrease in CV-RMSE when the target building has limited operational data.Even if the target building has no operational data,the federated model still achieves acceptable accuracy(CV-RMSE is 22.2%).Meanwhile,the training time of the federated model is 90%less than that of the standalone model.The research insights can help develop federated learning-based methods for solving the data silos problem in building energy management.The methodology and analysis procedures are reproducible and all codes and data sets are available on Github.

An energy consumption prediction approach of die casting machines driven by product parameters

Die casting machines,which are the core equipment of the machinery manufacturing industry,consume great amounts of energy.The energy consumption prediction of die casting machines can support energy consumption quota,process parameter energy-saving optimization,energy-saving design,and energy efficiency evaluation;thus,it is of great significance for Industry 4.0 and green manufacturing.Nevertheless,due to the uncertainty and complexity of the energy consumption in die casting machines,there is still a lack of an approach for energy consumption prediction that can provide support for process parameter optimization and product design taking energy efficiency into consideration.To fill this gap,this paper proposes an energy consumption prediction approach for die casting machines driven by product parameters.Firstly,the system boundary of energy consumption prediction is defined,and subsequently,based on the energy consumption characteristics analysis,a theoretical energy consumption model is established.Consequently,a systematic energy consumption prediction approach for die casting machines,involving product,die,equipment,and process parameters,is proposed.Finally,the feasibility and reliability of the proposed energy consumption prediction approach are verified with the help of three die casting machines and six types of products.The results show that the prediction accuracy of production time and energy consumption reached 91.64%and 85.55%,respectively.Overall,the proposed approach can be used for the energy consumption prediction of different die casting machines with different products.