NDLI: Very Short-Term Load Forecasting Based on Neural Network and Rough Set

Content Provider	IEEE Xplore Digital Library
Author	Pang Qingle Zhang Min
Copyright Year	2010
Abstract	The short-term load forecasting model based on neural network has been applied widely in energy management systems (EMS) because of its high forecasting accuracy and self-learning ability. But the forecasting errors of the load curve near peaks are large, especially at the large slope difference on both side of a peak. So the load forecasting based on rough set and neural network is proposed. The load in the current time interval, load in the previous time interval, load deviation between the current time interval and the previous time interval and current time is regarded as an input of a neural network respectively. The forecasting load at following time interval is the output of the neural network. The trained neural network is the load forecasting model based on neural network. Then, the forecasting load at following time interval obtained by the neural network based load forecasting model is compensated by rough set to increase the forecasting accuracy. The simulation experiments show that the presented load forecasting based on rough set and neural network can improve the forecasting accuracy significantly.
Starting Page	1132
Ending Page	1135
File Size	322269
Page Count	4
File Format	PDF
ISBN	9781424472796
e-ISBN	9781424472802
DOI	10.1109/ICICTA.2010.38
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2010-05-11
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Artificial neural networks Medical services Predictive models Rough Set Power system modeling Neural Network Load forecasting Neural networks Very Short-Term Load Forecasting Economic forecasting Power system reliability Autoregressive processes Load modeling
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 system load forecasting using an artificial neural network

Short-Term Load Forecasting Model For Power System Based on Complementation of Fuzzy-Rough Set Theory And BP Neural Network

Unit Commitment Scheduling by Using the Autoregressive and Artificial Neural Network Models Based Short-Term Load Forecasting

Intelligent short-term load forecasting based on pattern-base

An ARMA Cooperate with Artificial Neural Network Approach in Short-Term Load Forecasting

Short term load forecast using Burg autoregressive technique

A review of ANN-based short-term load forecasting models

A new method for short-term load forecasting integrating fuzzy-rough sets with artificial neural network

Short-Term Load Forecasting using Interval Arithmetic Backpropagation Neural Network

Very Short-Term Load Forecasting Based on Neural Network and Rough Set

Similar Documents

Short-term system load forecasting using an artificial neural network

Short-Term Load Forecasting Model For Power System Based on Complementation of Fuzzy-Rough Set Theory And BP Neural Network

Unit Commitment Scheduling by Using the Autoregressive and Artificial Neural Network Models Based Short-Term Load Forecasting

Intelligent short-term load forecasting based on pattern-base

An ARMA Cooperate with Artificial Neural Network Approach in Short-Term Load Forecasting

Short term load forecast using Burg autoregressive technique

A review of ANN-based short-term load forecasting models

A new method for short-term load forecasting integrating fuzzy-rough sets with artificial neural network

Short-Term Load Forecasting using Interval Arithmetic Backpropagation Neural Network

Very Short-Term Load Forecasting Based on Neural Network and Rough Set