Comparison of regression model and artificial neural network model for energy benchmarking of accommodation buildings in kanto, Japan

Haitham Alkhalaf, Wanglin Yan

Research output: Contribution to journalConference article


Energy performance of residential and non-residential buildings is a vital topic because of fast urbanization in the world. The accommodation buildings are considered as high energy intensive comparing to other commercial building' categories. In addition, it has an important contribution in tourism industry. Therefore, variety of models and plans have been applied to reduce the energy consumption of accommodation buildings. This research depends on database of energy consumption of commercial buildings in Japan as main source of data. Data-base for Energy Consumption of Commercial building (DECC) is a national survey, it is disclosed by Japan Sustainable Building Consortium (JSBC). Base on DECC, a benchmark system is developed by applying regression and Artificial Neural Network (ANN) methods to assess the energy performance of accommodation building in Kanto region-Japan. The study investigate the primary energy model of selected samples according to consumption' trends of electricity, gas and clean water. The developed benchmarks by ANN and regression models were compared to ensure a robust benchmark system as a powerful tool for energy performance' assessment. This study points out the necessity to benchmark the energy performance of accommodation buildings and other categories in Japan. In addition, it is important to consider other variables that affect energy use of buildings.

Original languageEnglish
Pages (from-to)71-82
Number of pages12
JournalWIT Transactions on Ecology and the Environment
Issue number1
Publication statusPublished - 2017 Sep 20
Event7th International conference on Energy and Sustainability, ESUS 2017 - Seville, Spain
Duration: 2017 Sep 202017 Sep 20



  • artificial neural network
  • benchmarking
  • DECC
  • energy performance
  • regression.

ASJC Scopus subject areas

  • Environmental Science(all)

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