Application Of BP Neural Network And Genetic Algorithm In Non-ferrous Metals Enterprise Energy Management

As one of the nine high energy-consuming industries, nonferrous metals industry is currently facing tremendous saving pressure from the energy, environment, national policies and markets. In the increasingly tight energy supply and the increasingly high cost environment, applying a scientific and effective method to manage enterprise energy is particularly important.Energy management system is an effective means for the scientific management of energy, in order to improve business efficiency and improve energy management systems, based on energy history data to statistical analysis for future energy intelligent forecasting to meet business needs.Therefore, the study of prediction model for energy saving target is of great significance.This study takes one non-ferrous metal enterprise in Foshan as the object. Aiming at the problems of energy waste or shortage resulting from the contradiction between energy demand and supply in this enterprise, and according to the study of the energy use of the enterprise, a corresponding energy consumption prediction model is built. And the model is integrated to the enterprise energy management system. It allows managers to set the energy distribution and the using plan in advance, meeting the needs of the energy in production.Firstly, based on the BP neural network model structure and learning algorithm, as well as the basis theory of the genetic algorithm, the genetic algorithm and BP neural network integration as a forecasting method to further research. Against the inadequacies of standard genetic algorithm for finding the optimal BP neural network weights and thresholds, introduction of Laplace crossover and golden mutation operators to improve the algorithm to increase the diversity of individuals, and enhance the speed of the algorithm optimization, thus to design a LGGA-BP neural network prediction model.Then, for the improved genetic algorithm and BP neural network their own advantages and characteristics, it takes use of LGGA-BP forecasting model to predict energy consumption of non-ferrous companies. In order to facilitate enterprises to develop a detailed energy plan, according to the production processes energy use characteristics in Non-ferrous metal enterprises, dividing and classifying the energy consumption of production processes for copper and using the LGGA-BP prediction model to forecast respectively.Finally, on the basis of the process energy consumption forecast, achieve effective day forecast for the entire company’s future energy consumption.The results show that relative to other forecasting results of the two models, LGGA-BP energy consumption prediction model has better optimization capabilities. Through the enterprise energy consumption prediction effectively, it provides support for energy production plan, reduces energy waste, and then realizes the energy supply and demand balance. And the prediction model is applied to the energy management system, making the energy management system used in non-ferrous metal enterprises obtain good effect.