Optimizing energy efficiency and thermal comfort in building green retrofit

Abstract Building green retrofit offers significant opportunities for enhancing energy efficiency and achieving green development goals. However, a conflicting criterion exists between energy conservation and thermal comfort improvement when making optimal design solutions for building retrofit. This study presents a simulation-based energy-comfort optimization model to facilitate evaluating various design alternatives and balancing multiple objectives in building green retrofit. A building simulation model is first established to measure energy consumption and comfort level. Then, a multi-objective optimization method (response surface method) is employed to identify critical building parameters and generates a set of alternative plans for building retrofit based on green building standards. After that, optimal design solutions with trade-offs between thermal comfort and energy demand are obtained. A school building in Wuhan city of China is chosen as a case to validate the developed model, and ten building parameters pertaining to energy demand and environmental comfort are considered in the optimization process. The results show that four parameters are significantly sensitive to energy efficiency and thermal comfort, including insulation thickness of the external wall, the heat transmission coefficient of the roof, solar heat gain coefficient of the external window, and window to wall ratio. The optimal combination of four parameters approximately produces 4 % of energy savings, as well as an improving level of environmental comfort. The study benefits designers and construction managers to determine optimal solutions for building retrofit to achieve better energy efficiency and comfort in green building development.

[1]  Benoit Boulet,et al.  Centralized Model Predictive Control Strategy for Thermal Comfort and Residential Energy Management , 2019, Energy.

[2]  Luis C. Dias,et al.  Multi-objective optimization for building retrofit: A model using genetic algorithm and artificial neural network and an application , 2014 .

[3]  G. Derringer,et al.  Simultaneous Optimization of Several Response Variables , 1980 .

[4]  V. Costanzo,et al.  The effect of passive measures on thermal comfort and energy conservation. A case study of the hot summer and cold winter climate in the Yangtze River region , 2018 .

[5]  Liu Yang,et al.  Thermal comfort and building energy consumption implications - A review , 2014 .

[6]  P. Lin,et al.  Green BIM Assessment Applying for Energy Consumption and Comfort in the Traditional Public Market: A Case Study , 2019, Sustainability.

[7]  Wei Yang,et al.  Application of Multi-Objective Genetic Algorithm Based Simulation for Cost-Effective Building Energy Efficiency Design and Thermal Comfort Improvement , 2018, Front. Energy Res..

[8]  Devendra Deshmukh,et al.  Statistical optimization using Central Composite Design for biomass and lipid productivity of microalga: A step towards enhanced biodiesel production , 2016 .

[9]  Paul Cooper,et al.  Existing building retrofits: Methodology and state-of-the-art , 2012 .

[10]  Limao Zhang,et al.  Data-driven estimation of building energy consumption with multi-source heterogeneous data , 2020 .

[11]  Samantha Hall,et al.  Development and initial trial of a tool to enable improved energy & human performance in existing commercial buildings , 2014 .

[12]  Ahmad Jrade,et al.  An Automated BIM Model to Conceptually Design, Analyze, Simulate, and Assess Sustainable Building Projects , 2014 .

[13]  Jing Lin,et al.  Cross-cultural assessment of the effectiveness of eco-feedback in building energy conservation , 2017 .

[14]  Luis C. Dias,et al.  A multi-objective optimization model for building retrofit strategies using TRNSYS simulations, GenOpt and MATLAB , 2012 .

[15]  Tarja Häkkinen,et al.  Reducing embodied carbon during the design process of buildings , 2015 .

[16]  Isam Shahrour,et al.  A simplified building thermal model for the optimization of energy consumption: Use of a random number generator , 2014 .

[17]  Burcin Becerik-Gerber,et al.  A model calibration framework for simultaneous multi-level building energy simulation , 2015 .

[18]  Fariborz Haghighat,et al.  Multiobjective optimization of building design using TRNSYS simulations, genetic algorithm, and Artificial Neural Network , 2010 .

[19]  Zerrin Yilmaz,et al.  A multi-criteria approach to affordable energy-efficient retrofit of primary school buildings , 2020 .

[20]  J. F. Nicol,et al.  Development of an adaptive thermal comfort model for energy-saving building design in Japan , 2020 .

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

[22]  John W. Fowler,et al.  Multiple response optimization using mixture-designed experiments and desirability functions in semiconductor scheduling , 2003 .

[23]  Jian Ge,et al.  Energy efficiency optimization strategies for university research buildings with hot summer and cold winter climate of China based on the adaptive thermal comfort , 2018, Journal of Building Engineering.

[24]  N. Zhu,et al.  Broadening human thermal comfort range based on short-term heat acclimation , 2019, Energy.

[25]  Navid Delgarm,et al.  Multi-objective optimization of building energy performance and indoor thermal comfort: A new method using artificial bee colony (ABC) , 2016 .

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

[27]  Baabak Ashuri,et al.  Valuation of energy efficient certificates in buildings , 2018 .

[28]  A. Zinatizadeh,et al.  Application of response surface methodology (RSM) to optimize coagulation-flocculation treatment of leachate using poly-aluminum chloride (PAC) and alum. , 2009, Journal of hazardous materials.

[29]  Zhang Lin,et al.  Response-surface-model-based system sizing for Nearly/Net zero energy buildings under uncertainty , 2018, Applied Energy.

[30]  Douglas C. Montgomery,et al.  Response Surface Methodology: Process and Product Optimization Using Designed Experiments , 1995 .

[31]  Sheikh Ahmad Zaki,et al.  Adaptive thermal comfort in university classrooms in Malaysia and Japan , 2017 .

[32]  Francesca Stazi,et al.  Super-insulated wooden envelopes in Mediterranean climate: Summer overheating, thermal comfort optimization, environmental impact on an Italian case study , 2017 .

[33]  M. Bezerra,et al.  Response surface methodology (RSM) as a tool for optimization in analytical chemistry. , 2008, Talanta.

[34]  T. Rajmohan,et al.  Multi-Response Optimization of Epoxidation Process Parameters of Rapeseed Oil Using Response Surface Methodology (RSM)-Based Desirability Analysis , 2014 .

[35]  Francesco Massa Gray,et al.  Assessment of the impact of HVAC system configuration and control zoning on thermal comfort and energy efficiency in flexible office spaces , 2020 .

[36]  Rehan Sadiq,et al.  Improving the energy efficiency of the existing building stock: A critical review of commercial and institutional buildings , 2016 .