Using a novel method to obtain heating energy benchmarks in a cold region of China for the preparation of formulating incentive energy policies

Abstract Increasing emphasis has been made on energy conservation and pollution control with regard to the urbanization and sustainable development in Chinese society. This requires the three parties involved, the government, the heating providers and the building occupants, to focus on energy policies that can encourage behavior towards energy saving. For this, the heating energy performance in the region should be evaluated and the energy benchmarks need to be extracted. This paper adopted a novel approach to derive the benchmarks of the existing building stock in a cold region of China. The benchmarking was done using a special technique based on the Lorenz curve, which was compared with two competing methods. The Lorenz curve method was proved to be the most adequate one among the three. It can extract the energy benchmarks based on the distribution rules of heating energy use intensity in relation to the cumulative building area. Besides, it is an efficient method when only limited building information or data is available. Based on the results, this study also discussed in what way policy incentives can focus on the relationship between heating fees and building energy performance in the severe cold climate zones of China.

[1]  Nance E. Matson,et al.  Review of California and National Methods for Energy PerformanceBenchmarking of Commercial Buildings , 2005 .

[2]  N. Zhu,et al.  Data and analytics for heating energy consumption of residential buildings: The case of a severe cold climate region of China , 2018, Energy and Buildings.

[3]  Francisco G. Montoya,et al.  Energy benchmarking for shopping centers in Gulf Coast region , 2016 .

[4]  Dongwei Yu,et al.  A two-step approach to forecasting city-wide building energy demand , 2018 .

[5]  Zhengwei Li,et al.  Methods for benchmarking building energy consumption against its past or intended performance: An overview , 2014 .

[6]  Daniel Tarlow,et al.  AUTOMATICALLY CALIBRATING A PROBABILISTIC GRAPHICAL MODEL OF BUILDING ENERGY CONSUMPTION , 2009 .

[7]  Kelvin K. W. Yau,et al.  Predicting electricity energy consumption: A comparison of regression analysis, decision tree and neural networks , 2007 .

[8]  Xiaofeng Guo,et al.  Modeling and forecasting building energy consumption: A review of data-driven techniques , 2019, Sustainable Cities and Society.

[9]  Björn Palm,et al.  Heating solutions for residential buildings in China: Current status and future outlook , 2018, Energy Conversion and Management.

[10]  Sylvain Robert,et al.  State of the art in building modelling and energy performances prediction: A review , 2013 .

[11]  Maria Kolokotroni,et al.  A method for energy classification of hotels: A case-study of Greece , 2012 .

[12]  David Hsu,et al.  Comparison of integrated clustering methods for accurate and stable prediction of building energy consumption data , 2015 .

[13]  Jing Zhao,et al.  Energy consumption quota of public buildings based on statistical analysis , 2012 .

[14]  Dejan Mumovic,et al.  Original Article/ResearchA comparative study of benchmarking approaches for non-domestic buildings: Part 1 – Top-down approach , 2013 .

[15]  Lijun Yu,et al.  Integrating willingness analysis into investment prediction model for large scale building energy saving retrofit: Using fuzzy multiple attribute decision making method with Monte Carlo simulation , 2019, Sustainable Cities and Society.

[16]  Kristian Fabbri,et al.  Energy performance building evaluation in Mediterranean countries: Comparison between software simulations and operating rating simulation , 2008 .

[17]  Tanveer Ahmad,et al.  Nonlinear autoregressive and random forest approaches to forecasting electricity load for utility energy management systems , 2019, Sustainable Cities and Society.

[18]  Taehoon Hong,et al.  Development of an integrated energy benchmark for a multi-family housing complex using district heating , 2016 .

[19]  Lee Siew Eang,et al.  Benchmarking energy use and greenhouse gas emissions in Singapore's hotel industry , 2010 .

[20]  Xiaofeng Li,et al.  Benchmarking energy performance for cooling in large commercial buildings , 2018, Energy and Buildings.

[21]  Sebastian Raschka,et al.  Python Machine Learning , 2015 .

[22]  Umberto Berardi,et al.  A data-driven approach for building energy benchmarking using the Lorenz curve , 2018, Energy and Buildings.

[23]  Chirag Deb,et al.  Determining key variables influencing energy consumption in office buildings through cluster analysis of pre- and post-retrofit building data , 2018 .

[24]  Jianzhong Wu,et al.  k-means based load estimation of domestic smart meter measurements , 2017 .

[25]  S. Ranji Ranjithan,et al.  Multivariate regression as an energy assessment tool in early building design , 2012 .

[26]  Z. Lian,et al.  A method to evaluate building energy consumption based on energy use index of different functional sectors , 2020 .

[27]  D. Kammen,et al.  Letting the (energy) Gini out of the bottle: Lorenz curves of cumulative electricity consumption and Gini coefficients as metrics of energy distribution and equity , 2005 .

[28]  Fu Xiao,et al.  Quantitative energy performance assessment methods for existing buildings , 2012 .

[29]  Mumine Gercek,et al.  Energy and environmental performance based decision support process for early design stages of residential buildings under climate change , 2019, Sustainable Cities and Society.

[30]  Xiaoling Zhang,et al.  Benchmarking the building energy consumption and solar energy trade-offs of residential neighborhoods on Chongming Eco-Island, China , 2016 .

[31]  G. Mihalakakou,et al.  On the heating potential of buried pipes techniques — application in Ireland , 1996 .

[32]  N. Zhou,et al.  Overview of current energy-efficiency policies in China , 2010 .

[33]  William Chung,et al.  Review of building energy-use performance benchmarking methodologies , 2011 .

[34]  B. Palm,et al.  Building heating solutions in China: A spatial techno-economic and environmental analysis , 2019, Energy Conversion and Management.

[35]  Luis Pérez-Lombard,et al.  A review of benchmarking, rating and labelling concepts within the framework of building energy certification schemes , 2009 .

[36]  Yibo Chen,et al.  Distribution patterns of energy consumed in classified public buildings through the data mining process , 2018 .

[37]  Xiaofan Zhao,et al.  Implementation of energy-saving policies in China: How local governments assisted industrial enterprises in achieving energy-saving targets , 2014 .

[38]  Wu Yong,et al.  Energy-efficiency supervision systems for energy management in large public buildings: Necessary choice for China , 2009 .

[39]  Umberto Berardi,et al.  Day-ahead prediction of hourly electric demand in non-stationary operated commercial buildings: A clustering-based hybrid approach , 2017 .

[40]  Yue-Jun Zhang,et al.  Exploring the direct rebound effect of residential electricity consumption: An empirical study in China , 2017 .

[41]  Michael White,et al.  Energy planning and forecasting approaches for supporting physical improvement strategies in the building sector: A review , 2016 .

[42]  M. N. Assimakopoulos,et al.  Using intelligent clustering techniques to classify the energy performance of school buildings , 2007 .

[43]  J. Gastwirth.  The Estimation of the Lorenz Curve and Gini Index , 1972 .

[44]  William Chung,et al.  Benchmarking the energy efficiency of commercial buildings , 2006 .

[45]  Hongting Ma,et al.  Energy consumption model and energy benchmarks of five-star hotels in China , 2018 .

[46]  Xiaoshu Lü,et al.  A novel dynamic modeling approach for predicting building energy performance , 2014 .