File(s) under permanent embargo
Load forecasting and neural networks : a prediction interval-based perspective
chapter
posted on 2010-01-01, 00:00 authored by Abbas KhosraviAbbas Khosravi, Saeid Nahavandi, Douglas CreightonDouglas CreightonSuccessfully 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.
History
Title of book
Computational intelligence in power engineeringSeries
Studies in Computational Intelligence; v. 302Chapter number
5Pagination
131 - 150Publisher
SpringerPlace of publication
Berlin, GermanyPublisher DOI
ISSN
1860-949XISBN-13
9783642140129ISBN-10
3642140122Language
engPublication classification
B1 Book chapterCopyright notice
2010, SpringerExtent
12Editor/Contributor(s)
B Panigrahi, A Abraham, S DasUsage metrics
Categories
No categories selectedKeywords
Licence
Exports
RefWorks
BibTeX
Ref. manager
Endnote
DataCite
NLM
DC