First order multivariate Markov chain model for generating annual weather data for Hong Kong

Hongxing Yang, Yutong Li, Lin Lu, Ronghui Qi

Research output: Journal article publicationJournal articleAcademic researchpeer-review

33 Citations (Scopus)


It becomes popular to use computer simulation technique to evaluate the performance of energy utilizations in buildings. The hourly weather data as simulation input is a crucial factor for the successful energy system simulation, and obtaining an accurate set of weather data is necessary to represent the long-term typical weather conditions throughout a year. This paper introduces a stochastic approach, which is called the first order multivariate Markov chain model, to generate the annual weather data for better evaluating and sizing different energy systems. The process for generating the weather data time series from the multivariate Markov transition probability matrices is described using 15-years actual hourly weather data of Hong Kong. The ability of this new model for retaining the statistical properties of the generated weather data series is examined and compared with the current existing TMY and TRY data. The main statistical properties used for this purpose are mean value, standard deviation, maximum value, minimum value, frequency distribution probability and persistency probability of the weather data sequence. The comparison between the observed weather data and the synthetically generated ones shows that the statistical characteristics of the developed set of weather data are satisfactorily preserved and the developed set of weather data can predict and evaluate different energy systems more accurately.
Original languageEnglish
Pages (from-to)2371-2377
Number of pages7
JournalEnergy and Buildings
Issue number9
Publication statusPublished - 1 Sept 2011


  • Annual weather data
  • Building simulation
  • Markov chain model
  • Transition probability matrix

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Building and Construction
  • Mechanical Engineering
  • Electrical and Electronic Engineering


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