NDLI: Online incremental regression for electricity price prediction

Content Provider	IEEE Xplore Digital Library
Author	Junchi Yan Chunhua Tian Yu Wang Jin Huang
Copyright Year	2012
Description	Author affiliation: IBM Research - China, Beijing, 100193, China (Junchi Yan; Chunhua Tian; Yu Wang; Jin Huang)
Abstract	Modeling methods aiming at predicting electricity price accurately, should be capable of handling a continuous stream of data while keeping responsive to the potential structural changes. To this end, traditional machine learning based approaches are widely applied such as Multi-linear Regression, Artificial Neural Network (ANN), Time Series Models like Auto Regressive Moving Average Models (ARMA), Gaussian Process (GP), random forests and Genetic Algorithm (GA), all of which can fall into two categories: the parametric and nonparametric model. While practical challenges in forecasting streaming data come along with the structural variation of the testing samples making the training samples not necessarily representative enough towards the new arriving samples. In such an online forecasting context, an incremental supervised learning based algorithm is better suited in contrast to the batch-mode one. Given the fact that it can adapt to the new coming streaming data by accommodating the possible variations of new samples, as well as allows for the removal of old data if necessary. An incremental learning algorithm is presented in this paper, i.e. the online support vector regression model, which enjoys the merits of less memory capacity and less computational overload compared with the batch methods. Promising results are demonstrated by evaluating with other typical regression methods for the electricity price forecasting task on a publicly available benchmark data set.
Starting Page	31
Ending Page	35
File Size	1636596
Page Count	5
File Format	PDF
ISBN	9781467324007
e-ISBN	9781467324014
DOI	10.1109/SOLI.2012.6273500
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2012-07-08
Publisher Place	China
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Adaptation models Computational modeling Weather forecasting Artificial neural networks Predictive models Incremental Support Vector Regression On-line Learning Incremental Regression Mathematical model MATLAB Price Forecasting
Content Type	Text
Resource Type	Article

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