Improved data-driven subspace algorithm for energy prediction in iron and steel industry

Using the production and energy system in Iron and Steel enterprise as the research background,we design a data-driven subspace(DDS) method for predicting the energy consumption of production operations.The characteristics of energy consumption and regeneration are fully investigated to find the crucial factors for building the model.The features of practical conditions and data are analyzed in designing the efficient solving method.The subspace method is improved by introducing the feedback factor and the forgetting factor,values of which are optimized by particle swarm optimization(PSO) algorithm in order to improve the prediction accuracy.The performance of the improved method is demonstrated by experimental tests using the practical data,which provide beneficial results in energy prediction and management.