Load forecasting and neural networks : a prediction interval-based perspective

Khosravi, Abbas, Nahavandi, Saeid and Creighton, Doug 2010, Load forecasting and neural networks : a prediction interval-based perspective. In Panigrahi, Bijaya Ketan, Abraham, Ajith and Das, Swagatam (ed), Computational intelligence in power engineering, Springer, Berlin, Germany, pp.131-150, doi: 10.1007/978-3-642-14013-6.

Attached Files
Name Description MIMEType Size Downloads

Title Load forecasting and neural networks : a prediction interval-based perspective
Author(s) Khosravi, AbbasORCID iD for Khosravi, Abbas orcid.org/0000-0001-6927-0744
Nahavandi, SaeidORCID iD for Nahavandi, Saeid orcid.org/0000-0002-0360-5270
Creighton, DougORCID iD for Creighton, Doug orcid.org/0000-0002-9217-1231
Title of book Computational intelligence in power engineering
Editor(s) Panigrahi, Bijaya Ketan
Abraham, Ajith
Das, Swagatam
Publication date 2010
Series Studies in Computational Intelligence; v. 302
Chapter number 5
Total chapters 12
Start page 131
End page 150
Total pages 20
Publisher Springer
Place of Publication Berlin, Germany
Summary Successfully determining competitive optimal schedules for electricity generation intimately hinges on the forecasts of loads. The nonstationarity and high volatility of loads make their accurate prediction somewhat problematic. Presence of uncertainty in data also significantly degrades accuracy of point predictions produced by deterministic load forecasting models. Therefore, operation planning utilizing these predictions will be unreliable. This paper aims at developing prediction intervals rather than producing exact point prediction. Prediction intervals are theatrically more reliable and practical than predicted values. The delta and Bayesian techniques for constructing prediction intervals for forecasted loads are implemented here. To objectively and comprehensively assess quality of constructed prediction intervals, a new index based on length and coverage probability of prediction intervals is developed. In experiments with real data, and through calculation of global statistics, it is shown that neural network point prediction performance is unreliable. In contrast, prediction intervals developed using the delta and Bayesian techniques are satisfactorily narrow, with a high coverage probability.
ISBN 9783642140129
3642140122
ISSN 1860-949X
Language eng
DOI 10.1007/978-3-642-14013-6
Field of Research 090602 Control Systems, Robotics and Automation
Socio Economic Objective 970109 Expanding Knowledge in Engineering
HERDC Research category B1 Book chapter
HERDC collection year 2010
Copyright notice ©2010, Springer
Persistent URL http://hdl.handle.net/10536/DRO/DU:30031437

Connect to link resolver
 
Unless expressly stated otherwise, the copyright for items in DRO is owned by the author, with all rights reserved.

Versions
Version Filter Type
Citation counts: TR Web of Science Citation Count  Cited 0 times in TR Web of Science
Scopus Citation Count Cited 19 times in Scopus
Google Scholar Search Google Scholar
Access Statistics: 1056 Abstract Views, 10 File Downloads  -  Detailed Statistics
Created: Fri, 03 Dec 2010, 14:44:58 EST by Sandra Dunoon

Every reasonable effort has been made to ensure that permission has been obtained for items included in DRO. If you believe that your rights have been infringed by this repository, please contact drosupport@deakin.edu.au.