NDLI: Insulator ESDD forecasting under complex climate conditions on the basis of LSSVM

Content Provider	IEEE Xplore Digital Library
Author	Haiyan Shuai Qingwu Gong
Copyright Year	2009
Description	Author affiliation: School of Electrical Engineering of Wuhan University, Ruojiashan Street, Wuhan City, 430072, China (Haiyan Shuai; Qingwu Gong)
Abstract	The paper, based on least squares support vector machines (LS-SVM), builds an intellectual prediction model whose input variables are temperature (T), relative humidity (H), wind velocity (WV), air pressure (P), rainfall(R) and equal salt deposit density (ESDD) measured one day before and the output variable is ESDD observed on the same day of the climate data, which are all provided by “Optical Sensor System for the ESDD Monitoring of Transmission Equipment” (OSSEMTE). In this model, the non-sensitive loss function is subrogated by quadratic loss function and the inequality constraints are substituted by equality constraints. Consequently, quadratic programming problem is simplified as the problem of solving linear equation groups, and the SVM algorithm is realized by least squares method. Through Grid Search Method, the optimal parameters of LS-SVM are selected automatically, which has improved the speed and accuracy of the forecasting. Compared to the BP simulated results, the predicted ESDD of the model are closer to the on-line measured ones. Therefore, the model presented supplies a feasible thread for the computerization of pollution distribution map of power network.
Starting Page	313
Ending Page	317
File Size	870787
Page Count	5
File Format	PDF
ISBN	9781424449019
DOI	10.1109/IDAACS.2009.5342972
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2009-09-21
Publisher Place	Italy
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Insulation Predictive models Wind forecasting Least squares methods Support vector machines Pollution measurement Input variables Temperature sensors Humidity Wind speed BP neural network ESDD forecasting least squares support vector machines (LS-SVM) complex climate conditions grid search method
Content Type	Text
Resource Type	Article

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

Insulators ESDD Predicting under Various Meteorological Conditions Based on Least Squares Support Vector Machines

Short-Term Power Load Forecasting Using Least Squares Support Vector Machines(LS-SVM)

Forecasting of wind speed with least squares support vector machine based on genetic algorithm

Short-Term Wind Speed and Output Power Forecasting Based on WT and LSSVM

A short-term wind speed forecasting model based on improved QPSO optimizing LSSVM

Short-term wind speed forecast of wind farm based on V-system and LSSVM

One-Month Ahead Prediction of Wind Speed and Output Power Based on EMD and LSSVM

Short-term wind speed prediction method based on time series combined with LS-SVM

Short-term wind speed prediction based on LS-SVM

Insulator ESDD forecasting under complex climate conditions on the basis of LSSVM

Similar Documents

Insulators ESDD Predicting under Various Meteorological Conditions Based on Least Squares Support Vector Machines

Short-Term Power Load Forecasting Using Least Squares Support Vector Machines(LS-SVM)

Forecasting of wind speed with least squares support vector machine based on genetic algorithm

Short-Term Wind Speed and Output Power Forecasting Based on WT and LSSVM

A short-term wind speed forecasting model based on improved QPSO optimizing LSSVM

Short-term wind speed forecast of wind farm based on V-system and LSSVM

One-Month Ahead Prediction of Wind Speed and Output Power Based on EMD and LSSVM

Short-term wind speed prediction method based on time series combined with LS-SVM

Short-term wind speed prediction based on LS-SVM

Insulator ESDD forecasting under complex climate conditions on the basis of LSSVM