Hourly cooling load forecasting using time-indexed ARX models with two-stage weighted least squares regression

Yin Guo, Ehsan Nazarian, Jeonghan Ko, Kamlakar Rajurkar

Research output: Contribution to journalArticle

50 Scopus citations

Abstract

This paper presents a robust hourly cooling-load forecasting method based on time-indexed autoregressive with exogenous inputs (ARX) models, in which the coefficients are estimated through a two-stage weighted least squares regression. The prediction method includes a combination of two separate time-indexed ARX models to improve prediction accuracy of the cooling load over different forecasting periods. The two-stage weighted least-squares regression approach in this study is robust to outliers and suitable for fast and adaptive coefficient estimation. The proposed method is tested on a large-scale central cooling system in an academic institution. The numerical case studies show the proposed prediction method performs better than some ANN and ARX forecasting models for the given test data set.

Original languageEnglish (US)
Pages (from-to)46-53
Number of pages8
JournalEnergy Conversion and Management
Volume80
DOIs
StatePublished - Apr 2014

Keywords

  • ARX
  • Cooling load
  • Forecasting
  • Weighted least squares

ASJC Scopus subject areas

  • Renewable Energy, Sustainability and the Environment
  • Nuclear Energy and Engineering
  • Fuel Technology
  • Energy Engineering and Power Technology

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