NDLI: Time series prediction using PSO-optimized neural network and hybrid feature selection algorithm for IEEE load data

Content Provider	Springer Nature Link
Author	Sheikhan, Mansour Mohammadi, Najmeh
Copyright Year	2012
Abstract	Artificial neural networks have been widely used in time series prediction. In this paper, multi-layer feedforward neural networks with optimized structures, using particle swarm optimization (PSO) algorithm, are used for hourly load prediction based on load time series of IEEE Reliability Test System. To have a small and appropriate feature subset, a hybrid method is used for feature selection in this paper. This hybrid method uses the combination of genetic algorithm (GA) and ant colony optimization (ACO) algorithm. The season, day of the week, time of the day and history load are considered as load influencing factors in this study based on the mentioned standard load dataset. The optimized number of neurons in the hidden layers of multi-layer perceptron (MLP) is determined using PSO algorithm. Experimental results show that the proposed hybrid model offers superior performance, in terms of mean absolute percentage error (MAPE), in time series prediction as compared to some recent researches in this field.
Starting Page	1185
Ending Page	1194
Page Count	10
File Format	PDF
ISSN	09410643
Journal	Neural Computing and Applications
Volume Number	23
Issue Number	3-4
e-ISSN	14333058
Language	English
Publisher	Springer London
Publisher Date	2012-06-07
Publisher Place	London
Access Restriction	One Nation One Subscription (ONOS)
Subject Keyword	Particle swarm optimization Neural network Time series prediction Feature selection Ant colony optimization Genetic algorithm Artificial Intelligence (incl. Robotics) Data Mining and Knowledge Discovery Probability and Statistics in Computer Science Computational Science and Engineering Image Processing and Computer Vision Computational Biology/Bioinformatics
Content Type	Text
Resource Type	Article
Subject	Artificial Intelligence Software

Sl.	Authority	Responsibilities	Communication Details
1	Ministry of Education (GoI), Department of Higher Education	Sanctioning Authority	https://www.education.gov.in/ict-initiatives
2	Indian Institute of Technology Kharagpur	Host Institute of the Project: The host institute of the project is responsible for providing infrastructure support and hosting the project	https://www.iitkgp.ac.in
3	National Digital Library of India Office, Indian Institute of Technology Kharagpur	The administrative and infrastructural headquarters of the project	Dr. B. Sutradhar bsutra@ndl.gov.in
4	Project PI / Joint PI	Principal Investigator and Joint Principal Investigators of the project	Dr. B. Sutradhar bsutra@ndl.gov.in Prof. Saswat Chakrabarti will be added soon
5	Website/Portal (Helpdesk)	Queries regarding NDLI and its services	support@ndl.gov.in
6	Contents and Copyright Issues	Queries related to content curation and copyright issues	content@ndl.gov.in
7	National Digital Library of India Club (NDLI Club)	Queries related to NDLI Club formation, support, user awareness program, seminar/symposium, collaboration, social media, promotion, and outreach	clubsupport@ndl.gov.in
8	Digital Preservation Centre (DPC)	Assistance with digitizing and archiving copyright-free printed books	dpc@ndl.gov.in
9	IDR Setup or Support	Queries related to establishment and support of Institutional Digital Repository (IDR) and IDR workshops	idr@ndl.gov.in

Erratum to: Time series prediction using PSO-optimized neural network and hybrid feature selection algorithm for IEEE load data

Erratum to: Time series prediction using PSO-optimized neural network and hybrid feature selection algorithm for IEEE load data

Time series prediction with improved neuro-endocrine model

Multiple parameter control for ant colony optimization applied to feature selection problem

Neural-based electricity load forecasting using hybrid of GA and ACO for feature selection

A bare-bones ant colony optimization algorithm that performs competitively on the sequential ordering problem

Ant-based and swarm-based clustering

Particle swarm optimization for feature selection with application in obstructive sleep apnea diagnosis

A link prediction algorithm based on ant colony optimization

Time series prediction using PSO-optimized neural network and hybrid feature selection algorithm for IEEE load data

Similar Documents

Erratum to: Time series prediction using PSO-optimized neural network and hybrid feature selection algorithm for IEEE load data

Erratum to: Time series prediction using PSO-optimized neural network and hybrid feature selection algorithm for IEEE load data

Time series prediction with improved neuro-endocrine model

Multiple parameter control for ant colony optimization applied to feature selection problem

Neural-based electricity load forecasting using hybrid of GA and ACO for feature selection

A bare-bones ant colony optimization algorithm that performs competitively on the sequential ordering problem

Ant-based and swarm-based clustering

Particle swarm optimization for feature selection with application in obstructive sleep apnea diagnosis

A link prediction algorithm based on ant colony optimization

Time series prediction using PSO-optimized neural network and hybrid feature selection algorithm for IEEE load data