BP Neural Network Combination Prediction for Big Data Enterprise Energy Management System

Abstract

The energy consumption of an enterprise energy management system (EMS) is a complex process with nonlinearity, time-variance, larger delay, greater inertia and other dynamic characteristics, resulting in the failure of a single-item prediction model to achieve satisfactory prediction results. In this paper, a combination prediction method, based on BP neural network, was proposed to predict the energy consumption of an enterprise EMS for improving the prediction accuracy. The energy consumption of enterprise energy management system (EMS) was predicted and analyzed using gray combination models, i.e., GM (1.1) and pGM (1.1), gray Markov chain, and BP neural network prediction model. These single-item models and their prediction processes were constructed and successfully applied to predict the energy consumption of iron and steel enterprises. The data pertaining to energy consumption of these enterprises from January to December 2018 and January to March 2019 were used for predicting the simulation and testing, respectively. The results showed that the prediction results of our approach has an average relative error of 3.327% and 1.298% respectively, which are extremely lower than the existing approaches for improving the prediction accuracy.