Forecasting Electrical Energy Consumption of Equipment Maintenance Using Neural Network and Particle Swarm Optimization
Joint Authors
Jiang, Xunlin
Ling, Haifeng
Yan, Jun
Li, Bo
Li, Zhao
Source
Mathematical Problems in Engineering
Issue
Vol. 2013, Issue 2013 (31 Dec. 2013), pp.1-8, 8 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2013-10-31
Country of Publication
Egypt
No. of Pages
8
Main Subjects
Abstract EN
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.
American Psychological Association (APA)
Jiang, Xunlin& Ling, Haifeng& Yan, Jun& Li, Bo& Li, Zhao. 2013. Forecasting Electrical Energy Consumption of Equipment Maintenance Using Neural Network and Particle Swarm Optimization. Mathematical Problems in Engineering،Vol. 2013, no. 2013, pp.1-8.
https://search.emarefa.net/detail/BIM-1008656
Modern Language Association (MLA)
Jiang, Xunlin…[et al.]. Forecasting Electrical Energy Consumption of Equipment Maintenance Using Neural Network and Particle Swarm Optimization. Mathematical Problems in Engineering No. 2013 (2013), pp.1-8.
https://search.emarefa.net/detail/BIM-1008656
American Medical Association (AMA)
Jiang, Xunlin& Ling, Haifeng& Yan, Jun& Li, Bo& Li, Zhao. Forecasting Electrical Energy Consumption of Equipment Maintenance Using Neural Network and Particle Swarm Optimization. Mathematical Problems in Engineering. 2013. Vol. 2013, no. 2013, pp.1-8.
https://search.emarefa.net/detail/BIM-1008656
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1008656