Energy consumption analysis and saving of buildings based on static and dynamic input-output models

Abstract With the increase of building area, energy consumption of building and carbon dioxide (CO2) emissions have been increased. Reducing the energy consumption of buildings has an enormous impact on the sustainable development of the world economy and social. Therefore, this paper proposes a novel energy consumption analysis and saving method of the building based on static and dynamic input-output models. The input-output based data envelopment analysis (DEA) is used to analyze the energy consumption of the building statically. Then the slack variables obtained by the DEA are used to improve efficiency values of ineffective days in months of buildings. Moreover, the total factor productivity (TFP) with technical efficiency (EFFCH), technical progress (TECHCH), pure technical efficiency (PECH), scale efficiency (SECH) and the total factor productivity change (TFPCH) of the building is dynamically analyzed by the Malmquist index. Finally, the static and dynamic energy consumption analysis and saving method is used to improve the energy efficiency of the building. Furthermore, when the technical progress by 1%, the total factor productivity change of the building can be improved by 1.042%.

[1]  Dylan Jones,et al.  A cross-European efficiency assessment of offshore wind farms: A DEA approach , 2020 .

[2]  W. Cai,et al.  EKC Analysis and Decomposition of Influencing Factors in Building Energy Consumption of Three Municipalities in China , 2018 .

[3]  Marcelo Resende,et al.  Service Quality in Electricity Distribution in Brazil: A Malmquist Approach , 2017, Annals of Public and Cooperative Economics.

[5]  S. Haider,et al.  Benchmarking energy use of iron and steel industry: a data envelopment analysis , 2019, Benchmarking: An International Journal.

[6]  Qian-Ru Yang,et al.  Total-Factor Energy Efficiency (TFEE) Evaluation on Thermal Power Industry with DEA, Malmquist and Multiple Regression Techniques , 2017 .

[7]  Bin Zhang,et al.  Energy and CO 2 emissions efficiency of major economies: A network DEA approach , 2018 .

[8]  Yongming Han,et al.  Energy analysis and resources optimization of complex chemical processes: Evidence based on novel DEA cross-model , 2021 .

[9]  P. Bertoldi,et al.  Analysis of the EU Residential Energy Consumption: Trends and Determinants , 2019, Energies.

[10]  Abraham Charnes,et al.  Measuring the efficiency of decision making units , 1978 .

[11]  M. Yu. Derevyanov,et al.  Using DEA models to measure the efficiency of energy saving projects , 2019 .

[12]  Kaamran Raahemifar,et al.  Application of passive wall systems for improving the energy efficiency in buildings: A comprehensive review , 2016 .

[13]  Joe Zhu,et al.  Super-efficiency and DEA sensitivity analysis , 2001, Eur. J. Oper. Res..

[14]  Necmi K. Avkiran,et al.  Stability and integrity tests in data envelopment analysis , 2007 .

[15]  Javed Ahmad Bhat,et al.  Inter-state analysis of energy efficiency- a stochastic frontier approach to the Indian paper industry , 2018, International Journal of Energy Sector Management.

[16]  Lizhan Cao,et al.  China’s Industrial Total-Factor Energy Productivity Growth at Sub-Industry Level: A Two-Step Stochastic Metafrontier Malmquist Index Approach , 2017 .

[17]  Pedro Paulo Balestrassi,et al.  Economic planning of wind farms from a NBI-RSM-DEA multiobjective programming , 2020 .

[18]  Paul W. Wilson,et al.  Dimension reduction in nonparametric models of production , 2017, Eur. J. Oper. Res..

[19]  A. Călin,et al.  Regional carbon emission efficiency and its dynamic evolution in China: A novel cross efficiency-malmquist productivity index , 2019 .

[20]  Kevin J. Fox,et al.  Decomposing productivity indexes into explanatory factors , 2017, Eur. J. Oper. Res..

[21]  C.A.K. Lovell,et al.  Multilateral Productivity Comparisons When Some Outputs are Undesirable: A Nonparametric Approach , 1989 .

[22]  Jing Du,et al.  Interaction effects of building technology and resident behavior on energy consumption in residential buildings , 2017 .

[23]  Javed Ahmad Bhat,et al.  Does total factor productivity affect the energy efficiency , 2019 .

[24]  Søren Knudsen Kær,et al.  Simulation of Thermal Behaviour of a Lithium Titanate Oxide Battery , 2019, Energies.

[25]  Xianguo Wu,et al.  Energy performance optimisation of building envelope retrofit through integrated orthogonal arrays with data envelopment analysis , 2020 .

[26]  H. Burak Gunay,et al.  Simulating occupants' impact on building energy performance at different spatial scales , 2018 .

[27]  Gunwon Lee,et al.  Impact of Urban and Building Form and Microclimate on the Energy Consumption of Buildings - Based on Statistical Analysis- , 2017 .

[28]  H. Izadbakhsh,et al.  A new reliable performance evaluation model: IFB-IER–DEA , 2019 .

[29]  Negative carbon dioxide emissions , 2020 .

[30]  Agha Iqbal Ali Data envelopment analysis: Computational issues , 1990 .

[31]  Zhiqiang Geng,et al.  Performance Analysis of China Ethylene Plants by Measuring Malmquist Production Efficiency Based on an Improved Data Envelopment Analysis Cross-Model , 2015 .

[32]  Yi-Ming Wei,et al.  Energy economics and climate policy modeling , 2017, Ann. Oper. Res..

[33]  Q. Jin,et al.  Efficiency assessment of rural domestic sewage treatment facilities by a slacked-based DEA model , 2020, Journal of Cleaner Production.

[34]  Elisabetta Allevi,et al.  Measuring the environmental performance of green SRI funds: A DEA approach , 2019, Energy Economics.

[35]  Yongming Han,et al.  Input-output networks considering graphlet-based analysis for production optimization: Application in ethylene plants , 2021 .

[36]  S. Malmquist Index numbers and indifference surfaces , 1953 .

[37]  Yongming Han,et al.  Production capacity identification and analysis using novel multivariate nonlinear regression: Application to resource optimization of industrial processes , 2020 .

[38]  S. Haider,et al.  Does Energy Efficiency Enhance Total Factor Productivity in Case of India? , 2017 .

[39]  Hirofumi Fukuyama,et al.  Modelling bank performance: A network DEA approach , 2017, Eur. J. Oper. Res..

[40]  Luis M. Candanedo,et al.  Data driven prediction models of energy use of appliances in a low-energy house , 2017 .

[41]  Yongming Han,et al.  Static and dynamic energy structure analysis in the world for resource optimization using total factor productivity method based on slacks-based measure integrating data envelopment analysis , 2021 .