NDLI: A hybrid approach of ultra-short term multinode load forecasting

Content Provider	IEEE Xplore Digital Library
Author	Li Han Xueshan Han Jian Hua Yaii Geng
Copyright Year	2007
Description	Author affiliation: Sch. of Electr. Eng., Shandong Univ., Jinan (Li Han; Xueshan Han; Jian Hua; Yaii Geng)
Abstract	In the electric power system, grasping active and reactive load variation regularity of each node is a key for optimal dispatch, preventive control, security assessment, and transmission capacity evaluation, etc. This paper presents an approach of ultra-short forecasting for multi-node active and reactive load on the basis of former researches. It proposes a hierarchy and subarea idea to construct a framework of self- adapting dynamic model. With the top layer load forecasting implemented by recursive least square support vector machines (RLS-SVM) algorithm, discrete state-space equations are established to describe dynamic characteristics of multi-node load parameters (active load distribution factors and power factors), and then TS fuzzy control technique is introduced to realize feedback compensation. The application in an actual power system control center of Shandong province has been verified with satisfactory result.
Starting Page	1321
Ending Page	1326
File Size	1277211
Page Count	6
File Format	PDF
ISBN	9789810594237
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2007-12-03
Publisher Place	Singapore
Access Restriction	Subscribed
Rights Holder	Nanyang Technological University
Subject Keyword	kalman filtering support vector machines Power system dynamics Control systems Power system security Least squares methods Equations multi-node Support vector machines fuzzy control Reactive power Load forecasting Optimal control load forecasting Load management power systems
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

Short-term load forecasting based on least square support vector machine combined with fuzzy control

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

Short-term power load forecasting with least squares support vector machines and wavelet transform

Application of Support Vector Regression in Power System Short Term Load Forecasting

Short-term electrical load forecasting using least squares support vector machines

Using least squares support vector machines in short-term electrical load forecasting

Power Load Forecasting with Least Squares Support Vector Machines and Chaos Theory

Short-Term Load Forecasting Based on Fuzzy C-Mean Clustering and Weighted Support Vector Machines

An effective hybrid approach for Dynamic State Estimation in Power System

A hybrid approach of ultra-short term multinode load forecasting

Similar Documents

Short-term load forecasting based on least square support vector machine combined with fuzzy control

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

Short-term power load forecasting with least squares support vector machines and wavelet transform

Application of Support Vector Regression in Power System Short Term Load Forecasting

Short-term electrical load forecasting using least squares support vector machines

Using least squares support vector machines in short-term electrical load forecasting

Power Load Forecasting with Least Squares Support Vector Machines and Chaos Theory

Short-Term Load Forecasting Based on Fuzzy C-Mean Clustering and Weighted Support Vector Machines

An effective hybrid approach for Dynamic State Estimation in Power System

A hybrid approach of ultra-short term multinode load forecasting