Research And System Implementation Of Energy Consumption Forecasting Method In Paint Shop

With the rapid development of the manufacturing industry,the demand for energy is growing,and the problem of energy consumption is becoming increasingly serious.Painting is one of the important processes in automobile manufacturing.The energy in the production process of the painting workshop is supplied on demand,and problems with energy supply will not only cause abnormal production operation of the workshop,but also cause energy waste,which will affect the economic benefits of the enterprise.Therefore,it is necessary to accurately grasp the energy demand to ensure painting Energy supply and demand balance in the workshop.For this reason,it is urgent to develop a high-efficiency and high-accuracy energy consumption prediction method,accurately predict the trend of energy consumption in the workshop,control the energy reserve,and provide a basis for the energy-saving optimization of each system in the painting workshop.This article takes the paint shop as the research object,establishes an energy consumption prediction model,and develops an energy management system for the paint shop.The specific research content is as follows:(1)Study the architecture of the coating energy management system.Based on the analysis of the energy consumption data status,process and energy consumption characteristics of the paint shop,the architecture of the paint shop energy management system is proposed,and the data collection and influence factors analysis of the paint shop energy management system are presented.Four key technologies,including feature extraction and energy consumption forecasting,have performed demand analysis.(2)Study the data acquisition and feature extraction methods of the paint shop.By analyzing the energy consumption characteristics of the painting workshop,the relevant parameters to be collected are clarified;the real-time data of the painting workshop is obtained by using OPC UA technology;and then an influencing factor analysis method based on Pearson-PCA is proposed to screen the factors affecting energy consumption.The feature extraction of energy consumption data based on improved EMD method is studied.(3)Study the energy consumption prediction technology of the paint shop.According to the characteristics of energy consumption data in the painting workshop,a LSTM-CNN prediction method based on Bayesian optimization was proposed.The LSTM was used to predict the energy consumption data characteristics and combined with CNN to reconstruct the energy consumption characteristics.The model is used as the energy consumption prediction model of the painting workshop.The energy consumption of the painting workshop is predicted by inputting historical energy consumption and influencing factors.(4)Developed a paint shop energy management system based on the energy consumption prediction model.Using Python,HTML,JS and other development tools to develop a paint shop energy management system.The energy consumption statistics,energy consumption prediction,and equipment management of the painting workshop were completed,which met the actual needs of the enterprise.