Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2013, Article ID 194730, 8 pages
Research Article

Forecasting Electrical Energy Consumption of Equipment Maintenance Using Neural Network and Particle Swarm Optimization

College of Field Engineering, PLA University of Science and Technology, Nanjing 210007, China

Received 1 June 2013; Revised 17 August 2013; Accepted 9 September 2013

Academic Editor: Yi-Chung Hu

Copyright © 2013 Xunlin Jiang et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


Accurate forecasting of electrical energy consumption of equipment maintenance plays an important role in maintenance decision making and helps greatly in sustainable energy use. The paper presents an approach for forecasting electrical energy consumption of equipment maintenance based on artificial neural network (ANN) and particle swarm optimization (PSO). A multilayer forward ANN is used for modeling relationships between the input variables and the expected electrical energy consumption, and a new adaptive PSO algorithm is proposed for optimizing the parameters of the ANN. Experimental results demonstrate that our approach provides much better accuracies than some other competitive methods on the test data.