Forecasting short-term electricity consumption using the adaptive grey-based approach-An Asian case

Der Chiang Li, Che Jung Chang, Chien Chih Chen, Wen Chih Chen

Research output: Contribution to journalArticlepeer-review

153 Citations (Scopus)

Abstract

The overall electricity consumption, treated as a primary guideline for electricity system planning, is a major measurement to indicate the degree of a nation's development. The electricity consumption forecast is especially important with regard to policy making in developing countries (Asian countries in this work). However, since the economic growth rates in these countries are usually high and unstable, it is difficult to obtain accurate predictions using long-term data, and thus forecasting with limited (short-term) data is more effective and of considerable interest. Grey theory is one approach that can be used to construct a model with limited samples to provide better forecasting advantage for short-term problems. The forecasting performance of AGM(1,1), based on grey theory, has been confirmed using the Asia-Pacific economic cooperation energy database, and the results, compared with those obtained from back propagation neural networks (BPN) and support vector regression (SVR), show that the proposed approach can effectively deal with the problem of forecasting electricity consumption when the sample size is limited.

Original languageEnglish
Pages (from-to)767-773
Number of pages7
JournalOmega
Volume40
Issue number6
DOIs
Publication statusPublished - 2012 Dec

All Science Journal Classification (ASJC) codes

  • Strategy and Management
  • Management Science and Operations Research
  • Information Systems and Management

Fingerprint Dive into the research topics of 'Forecasting short-term electricity consumption using the adaptive grey-based approach-An Asian case'. Together they form a unique fingerprint.

Cite this