A Prediction Method Of Energy Consumption Based On Deep Belief Network And Support System For Machining Workshops

Manufacturing industry occupies an important share of the national economy and directly reflects the productivity level of a country.Although it promotes the growth of the global economy,it also consumes a lot of energy and resources and causes pollution to the environment.Mechanical processing workshops are an important part of the manufacturing industry,and their energy consumption is becoming increasingly serious.Through the prediction of the energy consumption of the machine shop,it is possible not only to grasp the trend of energy consumption,to control the storage of energy,to reduce the energy waste,and to reduce the production cost of the workshop,but also to be the basis for energy-saving research in the workshop,and to optimize energy consumption in the workshop.Provide valid basis.Therefore,this paper analyzes the energy consumption and energy consumption of mechanical processing plant,proposes a prediction method based on deep belief network for the energy consumption of machine shop,and develops a support system based on this energy consumption forecasting method.The main research contents of the paper are as follows:First of all,according to the characteristics of the multi-energy consumption components of the machining shop,the energy consumption of the machining shop is analyzed from the perspective of the equipment level.Based on this,the index system of energy consumption in the machining shop is established,which is used as the input of the energy consumption prediction model.The type of variable,and analysis of energy consumption influencing factors.Secondly,on the basis of the above,a prediction method for energy consumption of machining shop based on deep belief network is proposed.First preprocess the original sample data,divide the sample data into training data and test data,design the network structure according to the data and training objectives,and then train the sample data to obtain the weight and bias of each layer of neurons.Quantity,through the error of the training result,judge whether the model training is successful,and finally use the trained model to predict the test sample data.After the prediction is completed,the error analysis of the prediction result is performed.According to the results of error analysis,continuous training of the model,adjustment of the model’s network structure and training parameters,until the minimum prediction error,the model as a final prediction model.This paper compares the energy prediction model of this paper with the predic-tion model based on shallow neural network and the prediction model based on support vector machine.Finally,a support system for energy consumption prediction methods based on deep belief network was developed.The architecture and function modules of the support system were designed first;then this support system was implemented through the integration of C# language and MATLAB algorithm;and through this The support system shows the prediction effect of the energy consumption prediction method based on the deep belief network in the machining shop,and proves the effectiveness of the system.