NDLI: Forecasting increasing rate of power consumption based on immune genetic algorithm combined with neural network

Content Provider	Springer Nature Link
Author	Yang, Shu xia
Copyright Year	2008
Abstract	Considering the factors affecting the increasing rate of power consumption, the BP neural network structure and the neural network forecasting model of the increasing rate of power consumption were established. Immune genetic algorithm was applied to optimizing the weight from input layer to hidden layer, from hidden layer to output layer, and the threshold value of neuron nodes in hidden and output layers. Finally, training the related data of the increasing rate of power consumption from 1980 to 2000 in China, a nonlinear network model between the increasing rate of power consumption and influencing factors was obtained. The model was adopted to forecasting the increasing rate of power consumption from 2001 to 2005, and the average absolute error ratio of forecasting results is 13.521 8%. Compared with the ordinary neural network optimized by genetic algorithm, the results show that this method has better forecasting accuracy and stability for forecasting the increasing rate of power consumption.
Starting Page	327
Ending Page	330
Page Count	4
File Format	PDF
ISSN	10059784
Journal	Journal of Central South University of Technology
Volume Number	15
Issue Number	2
e-ISSN	19930666
Language	English
Publisher	Central South University
Publisher Date	2010-04-06
Publisher Place	Heidelberg
Access Restriction	Subscribed
Subject Keyword	immune genetic algorithm neural network power consumption increasing rate forecast Metallic Materials Engineering
Content Type	Text
Resource Type	Article
Subject	Mechanics of Materials Materials Science Mechanical Engineering

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

Optimizing neural network forecast by immune algorithm

Improved wavelet neural network combined with particle swarm optimization algorithm and its application

A new grey forecasting model based on BP neural network and Markov chain

Power Demand Forecast Based on Optimized Neural Networks by Improved Genetic Algorithm

A novel recurrent neural network forecasting model for power intelligence center

Electricity price forecasting using generalized regression neural network based on principal components analysis

Study of Power System Short-term Load Forecast Based on Artificial Neural Network and Genetic Algorithm

Short circuit current forecast of large scale power grid based on improving BP Artificial Neural Network combined with Genetic Algorithm

Power system short-term load forecasting based on neural network with artificial immune algorithm

Forecasting increasing rate of power consumption based on immune genetic algorithm combined with neural network

Similar Documents

Optimizing neural network forecast by immune algorithm

Improved wavelet neural network combined with particle swarm optimization algorithm and its application

A new grey forecasting model based on BP neural network and Markov chain

Power Demand Forecast Based on Optimized Neural Networks by Improved Genetic Algorithm

A novel recurrent neural network forecasting model for power intelligence center

Electricity price forecasting using generalized regression neural network based on principal components analysis

Study of Power System Short-term Load Forecast Based on Artificial Neural Network and Genetic Algorithm

Short circuit current forecast of large scale power grid based on improving BP Artificial Neural Network combined with Genetic Algorithm

Power system short-term load forecasting based on neural network with artificial immune algorithm

Forecasting increasing rate of power consumption based on immune genetic algorithm combined with neural network