Forecasting the electricity consumption of commercial sector in Hong Kong using a novel grey dynamic prediction model

Bo Zeng, Yongtao Tan, Hui Xu, Jing Quan, Luyun Wang, Xueyu Zhou

Research output: Journal article publicationJournal articleAcademic researchpeer-review

20 Citations (Scopus)

Abstract

Energy is a critical component that underpins economic development. In Hong Kong, the commercial sector account for more than 60% of the total electricity consumption. With economic development, there will be an increasing demand for electricity, especially the commercial sector. Therefore, it is necessary to forecast the electricity consumption in the future and find possible solutions to meet the increasing demand. In this paper, a novel grey dynamic prediction model (GDPM) is proposed to forecast the electricity consumption of commercial sector in Hong Kong. Six models, including GDPM are used, respectively, to simulate the electricity consumption of commercial sector in Hong Kong during Years 1997-2008 and forecast the consumption during Years 2009-2015. The results show that the proposed GDPM has better forecasting performance than the other five models. Furthermore, the GDPM is used to forecast the electricity consumption of Hong Kong over the next five years. The forecast findings show the total electricity consumption of commercial sector will reach to 108050.0 Terajoule in 2020. Therefore, it is necessary for the Hong Kong Government to think about how to meet the increasing electricity demand.
Original languageEnglish
Pages (from-to)159-174
Number of pages16
JournalJournal of Grey System
Volume30
Issue number1
Publication statusPublished - 1 Jan 2018

Keywords

  • Commercial sector
  • Electricity consumption forecasting
  • Grey dynamic prediction model (GDPM)
  • Hong Kong

ASJC Scopus subject areas

  • Modelling and Simulation
  • Statistics, Probability and Uncertainty
  • Control and Optimization
  • Applied Mathematics

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