A multi-objective optimization model for energy-efficiency building envelope retrofitting plan with rooftop PV system installation and maintenance

Retrofitting existing buildings with energy-efficient facilities is an effective method to improve their energy efficiency, especially for old buildings. A multi-objective optimization model for building envelope retrofitting is presented. Envelope components including windows, external walls and roofs are considered to be retrofitted. Installation of a rooftop solar panel system is also taken into consideration in this study. Rooftop solar panels are modeled with their degradation and a maintenance scheme is studied for sustainability of energy and its long-term effect on the retrofitting plan. The purpose is to make the best use of financial investment to maximize energy savings and economic benefits. In particular, net present value, the payback period and energy savings are taken as the main performance indicators of the retrofitting plan. The multi-objective optimization problem is formulated as a non-linear integer programming problem and solved by a weighted sum method. Results of applying the designed retrofitting plan to a 50-year-old building consisting of 66 apartments demonstrated the effectiveness of the proposed model.

[1]  Hongxing Yang,et al.  The application of air layers in building envelopes: A review , 2016 .

[2]  E. Martinot,et al.  World Bank/GEF Solar Home Systems Projects : experiences and lessons learned 1993-2000 , 2000 .

[3]  I. Kim,et al.  Adaptive weighted sum method for multiobjective optimization: a new method for Pareto front generation , 2006 .

[4]  Darrell Whitley,et al.  A genetic algorithm tutorial , 1994, Statistics and Computing.

[5]  Hyo Seon Park,et al.  An estimation model for the heating and cooling demand of a residential building with a different envelope design using the finite element method , 2014 .

[6]  Karen Holm Olsen,et al.  The clean development mechanism’s contribution to sustainable development: a review of the literature , 2007 .

[7]  F. Bruno,et al.  Impact of climate change on the design of energy efficient residential building envelopes , 2015 .

[8]  Alam Hossain Mondal,et al.  Impacts of solar home systems on social development in rural Bangladesh , 2011 .

[9]  Jie Pengfei,et al.  Determination of the insulation thickness for the residential building envelope in severe cold areas , 2012, 2012 2nd International Conference on Consumer Electronics, Communications and Networks (CECNet).

[10]  Xiaohua Xia,et al.  Optimal control of maintenance instants and intensities in building energy efficiency retrofitting project , 2015, 2015 54th IEEE Conference on Decision and Control (CDC).

[11]  Liwei Tian,et al.  Evaluation on energy and thermal performance for office building envelope in different climate zones of China , 2015 .

[12]  B. M. Suleiman,et al.  Estimation of U-value of traditional North African houses , 2011 .

[13]  Mario Sassone,et al.  The early design stage of a building envelope: Multi-objective search through heating, cooling and lighting energy performance analysis , 2015 .

[14]  H. Khatib.  IEA World Energy Outlook 2011—A comment , 2012 .

[15]  Kalyanmoy Deb,et al.  A fast and elitist multiobjective genetic algorithm: NSGA-II , 2002, IEEE Trans. Evol. Comput..

[16]  Mohan M. Kumaraswamy,et al.  Informing Energy-efficient Building Envelope Design Decisions for Hong Kong , 2014 .

[17]  Dorota Chwieduk.  Some Aspects of Energy Efficient Building Envelope in High Latitude Countries , 2014 .

[18]  Bo Wang,et al.  Improving building energy efficiency by multiobjective neighborhood field optimization , 2015 .

[19]  Xiaohua Xia,et al.  Optimal power flow management for distributed energy resources with batteries , 2015 .

[20]  Robert Černý,et al.  Computational assessment of thermal performance of contemporary ceramic blocks with complex internal geometry in building envelopes , 2015 .

[21]  Hervé Lequay,et al.  Development of polynomial regression models for composite dynamic envelopes’ thermal performance forecasting , 2013 .

[22]  Hisham Khatib,et al.  IEA World Energy Outlook 2010—A comment , 2011 .

[23]  Bo Wang,et al.  Optimal maintenance planning for building energy efficiency retrofitting from optimization and control system perspectives , 2015 .

[24]  Xiaohua Xia,et al.  A multi-objective optimization model for the life-cycle cost analysis and retrofitting planning of buildings , 2014 .

[25]  Leif Gustavsson,et al.  Owners perception on the adoption of building envelope energy efficiency measures in Swedish detached houses , 2010 .

[26]  B. Marion,et al.  Long-Term Performance of the SERF PV Systems , 2003 .

[27]  Xiaohua Xia,et al.  Optimal maintenance planning for sustainable energy efficiency lighting retrofit projects by a control system approach , 2015 .

[28]  Jianlei Niu,et al.  Study on performance of energy-efficient retrofitting measures on commercial building external walls in cooling-dominant cities , 2013 .

[29]  Xiaohua Xia,et al.  A Multi-objective Optimization Model for Building Envelope Retrofit Planning☆ , 2015 .

[30]  Luis C. Dias,et al.  Multi-objective optimization for building retrofit strategies: A model and an application , 2012 .

[31]  Xiaohua Xia,et al.  A multiple objective optimisation model for building energy efficiency investment decision , 2013 .

[32]  Xiaohua Xia,et al.  Maintenance plan optimization in building retrofitting with interacting energy efficiency effects , 2015, 2015 Chinese Automation Congress (CAC).

[33]  Hassam ur Rehman.  Experimental performance evaluation of solid concrete and dry insulation materials for passive buildings in hot and humid climatic conditions , 2017 .

[34]  D. Gossard,et al.  Multi-objective optimization of a building envelope for thermal performance using genetic algorithms and artificial neural network , 2013 .

[35]  X. Xia,et al.  Minimum cost solution of photovoltaic–diesel–battery hybrid power systems for remote consumers , 2013 .

[36]  Fariborz Haghighat,et al.  Designing building envelope with PCM wallboards: Design tool development , 2014 .

[37]  Xiaohua Xia,et al.  Switched Model Predictive Control for Energy Dispatching of a Photovoltaic-Diesel-Battery Hybrid Power System , 2015, IEEE Transactions on Control Systems Technology.

[38]  Maurizio Carlini,et al.  Simulating Heat Transfers through the Building Envelope: a Useful Tool in the Economical Assessment , 2014 .

[39]  Luís Bragança,et al.  Methodology to enhance the Portuguese thermal regulation accuracy for existing buildings , 2009 .

[40]  G. M. Revel,et al.  Cool products for building envelope – Part II: Experimental and numerical evaluation of thermal performances , 2014 .

[41]  Zhihua Zhou,et al.  The operational performance of “net zero energy building”: A study in China , 2016 .

[42]  Bo Wang,et al.  Large-scale building energy efficiency retrofit: Concept, model and control , 2016 .

[43]  Xiaohua Xia,et al.  Optimal sampling plan for clean development mechanism energy efficiency lighting projects , 2013 .

[44]  X. Xia,et al.  Demand side management of photovoltaic-battery hybrid system , 2015 .