JOURNAL ARTICLE

A non-linear ARMAX for short term load forecasting

Chen-Sung Chang

Year: 2009 Journal:   Journal of Statistics and Management Systems Vol: 12 (4)Pages: 749-758   Publisher: Taylor & Francis

Abstract

Abstract This paper presents a methodology for forecasting the hourly load demand of the power provider enterprise using a non-linear autoregressive moving average model with exogenous variable (NARMAX). The NARMAX method can use fewer order terms and more combination terms to capture the dynamic characteristics of highly non-linear system with orthogonal parameter estimation. And the NARMAX method is expected to be improved the model identification and fewer orders in the short term load forecasting. In this paper, the proposed NARMAX method is used to test on a practice power provider enterprise (Taiwan power supply networks) for short term load forecasting to illustrate its performance.

Keywords:
Autoregressive–moving-average model Moving average Term (time) Computer science Moving-average model Identification (biology) Autoregressive model Power (physics) Control theory (sociology) Econometrics Linear model Electric power system Mathematical optimization Autoregressive integrated moving average Time series Mathematics Control (management) Artificial intelligence

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18
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Topics

Energy Load and Power Forecasting
Physical Sciences →  Engineering →  Electrical and Electronic Engineering
Grey System Theory Applications
Social Sciences →  Decision Sciences →  Management Science and Operations Research
Image and Signal Denoising Methods
Physical Sciences →  Computer Science →  Computer Vision and Pattern Recognition

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