Title :
Prediction intervals for electricity load forecasting using neural networks
Author :
Rana, M.M. ; Koprinska, Irena ; Khosravi, Abbas ; Agelidis, Vassilios Georgios
Author_Institution :
Sch. of Inf. Technol., Univ. of Sydney, Sydney, NSW, Australia
Abstract :
Most of the research in time series is concerned with point forecasting. In this paper we focus on interval forecasting and its application for electricity load prediction. We extend the LUBE method, a neural network-based method for computing prediction intervals. The extended method, called LUBEX, includes an advanced feature selector and an ensemble of neural networks. Its performance is evaluated using Australian electricity load data for one year. The results showed that LUBEX is able to generate high quality prediction intervals, using a very small number of previous lag variables and having acceptable training time requirements. The use of ensemble is shown to be critical for the accuracy of the results.
Keywords :
load forecasting; neural nets; power engineering computing; time series; Australian electricity load data; LUBE method; LUBEX; advanced feature selector; electricity load forecasting; neural networks ensemble; prediction intervals; time series; Artificial neural networks; Computer architecture; Electricity; Forecasting; Reliability; Training; Training data;
Conference_Titel :
Neural Networks (IJCNN), The 2013 International Joint Conference on
Conference_Location :
Dallas, TX
Print_ISBN :
978-1-4673-6128-6
DOI :
10.1109/IJCNN.2013.6706839