Multi-stage and multi-objective optimization for energy retrofitting a developed hospital reference building: A new approach to assess cost-optimality

The ‘Energy Performance of Buildings Directive’ Recast (i.e., 2010/31/EU) establishes that building energy retrofit should pursue “cost-optimal levels”. However, a reliable and rigorous cost-optimal analysis is an arduous and computationally-expensive issue, especially for complex buildings such as hospitals. The paper tackles this issue by providing a novel methodology to identify robust cost-optimal energy retrofit solutions. Multi-stage and multi-objective (Pareto) optimization is performed with the aim of minimizing the computational burden required to achieve reliable outcomes. The methodology combines EnergyPlus and MATLAB® and includes two optimization stages, preceded by a preliminary energy investigation that performs Latin hypercube sampling and sensitivity analysis. The preliminary investigation and the first optimization stage, which runs a genetic algorithm, aim at detecting efficient energy retrofit measures (ERMs) to reduce thermal energy demand for space heating and cooling. In the second optimization stage, these ERMs are combined with further ERMs, addressed to improve the efficiency of energy systems and to exploit renewable energy sources. Investment cost, primary energy consumption and global cost related to the resulting retrofit packages are investigated by means of smart exhaustive sampling. Finally, the cost-optimal solution is identified both in presence of a limitless economic availability and of limited budgets. The methodology is applied to a hospital reference building (RB), which represents hospitals built in South Italy between 1991 and 2005. The RB is defined by using an original approach, as required by the complexity of the examined building category. The achieved cost-optimal retrofit packages imply a reduction of primary energy consumption up to 67.9kWh/m2a (12.2%) and of global cost up to 2932k€ (24.5%) with a maximum investment of 1236k€.

[1]  Wei Tian,et al.  A review of sensitivity analysis methods in building energy analysis , 2013 .

[2]  Fabio Polonara,et al.  State of the art of thermal storage for demand-side management , 2012 .

[3]  Weijun Gao,et al.  Optimal option of distributed generation technologies for various commercial buildings , 2009 .

[4]  Shuangzhe Liu,et al.  Global Sensitivity Analysis: The Primer by Andrea Saltelli, Marco Ratto, Terry Andres, Francesca Campolongo, Jessica Cariboni, Debora Gatelli, Michaela Saisana, Stefano Tarantola , 2008 .

[5]  Federico Silvestro,et al.  Electrical consumption forecasting in hospital facilities: An application case , 2015 .

[6]  Koray Korkmaz,et al.  An approach for developing sensitive design parameter guidelines to reduce the energy requirements of low-rise apartment buildings , 2012 .

[7]  Bing Liu,et al.  U.S. Department of Energy Commercial Reference Building Models of the National Building Stock , 2011 .

[8]  Min Xie,et al.  A systematic comparison of metamodeling techniques for simulation optimization in Decision Support Systems , 2010, Appl. Soft Comput..

[9]  Velimir Čongradac,et al.  Assessing the energy consumption for heating and cooling in hospitals , 2012 .

[10]  Manuel Duarte Pinheiro,et al.  A Portuguese approach to define reference buildings for cost-optimal methodologies , 2015 .

[11]  Giuseppe Peter Vanoli,et al.  Rehabilitation of the building envelope of hospitals: Achievable energy savings and microclimatic control on varying the HVAC systems in Mediterranean climates , 2013 .

[12]  Godfried Augenbroe,et al.  Analysis of uncertainty in building design evaluations and its implications , 2002 .

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

[14]  Jonathan A. Wright,et al.  Optimization of building thermal design and control by multi-criterion genetic algorithm , 2002 .

[15]  Pgs Paul Rutten,et al.  Thermal comfort and the integrated design of homes for older people with dementia , 2010 .

[16]  Antonio Costa,et al.  A mixed-integer linear programming (MILP) model for the evaluation of CHP system in the context of hospital structures , 2014 .

[17]  Aristides Kiprakis,et al.  A multi-objective approach for optimal prioritization of energy efficiency measures in buildings: Model, software and case studies , 2015 .

[18]  Gerardo Maria Mauro,et al.  Multi-objective optimization of the renewable energy mix for a building , 2016 .

[19]  Zheng O'Neill,et al.  A methodology for meta-model based optimization in building energy models , 2012 .

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

[21]  Anna Laura Pisello,et al.  A method for assessing buildings’ energy efficiency by dynamic simulation and experimental activity , 2012 .

[22]  Kalyanmoy Deb,et al.  Multi-objective optimization using evolutionary algorithms , 2001, Wiley-Interscience series in systems and optimization.

[23]  Harvey M. Wagner,et al.  Global Sensitivity Analysis , 1995, Oper. Res..

[24]  M. Fesanghary,et al.  Design of low-emission and energy-efficient residential buildings using a multi-objective optimization algorithm , 2012 .

[25]  Pgs Paul Rutten,et al.  The indoor environment and the integrated design of homes for older people with dementia , 2010 .

[26]  Francesco Calise,et al.  Dynamic energy performance analysis: Case study for energy efficiency retrofits of hospital buildings , 2014 .

[27]  Gerardo Maria Mauro,et al.  A new methodology for cost-optimal analysis by means of the multi-objective optimization of building energy performance , 2015 .

[28]  M. Hasanuzzaman,et al.  An end-use energy analysis in a Malaysian public hospital , 2010 .

[29]  Ralph Evins,et al.  A review of computational optimisation methods applied to sustainable building design , 2013 .

[30]  M. Hamdy,et al.  A multi-stage optimization method for cost-optimal and nearly-zero-energy building solutions in line with the EPBD-recast 2010 , 2013 .

[31]  Sepehr Sanaye,et al.  Modeling and multi-objective optimization of parallel flow condenser using evolutionary algorithm , 2011 .

[32]  Pieter de Wilde,et al.  Predicting the performance of an office under climate change: A study of metrics, sensitivity and zonal resolution , 2010 .

[33]  Farshad Kowsary,et al.  Multi-objective optimization of the building energy performance: A simulation-based approach by means of particle swarm optimization (PSO) , 2016 .

[34]  Teuku Meurah Indra Mahlia,et al.  Chillers energy consumption, energy savings and emission analysis in an institutional buildings , 2011 .

[35]  Katya Scheinberg,et al.  Introduction to derivative-free optimization , 2010, Math. Comput..

[36]  Velimir Čongradac,et al.  Methods for assessing energy savings in hospitals using various control techniques , 2014 .

[37]  Marzouk Benali,et al.  Multi-objective self-adaptive algorithm for highly constrained problems: Novel method and applications , 2010 .

[38]  Paola Caputo,et al.  Overcoming the inertia of building energy retrofit at municipal level: The Italian challenge , 2015 .

[39]  Jon C. Helton,et al.  Survey of sampling-based methods for uncertainty and sensitivity analysis , 2006, Reliab. Eng. Syst. Saf..

[40]  Inhan Kim,et al.  PROCESS-DRIVEN BIM-BASED OPTIMAL DESIGN USING INTEGRATION OF ENERGYPLUS, GENETIC ALGORITHM, AND PARETO OPTIMALITY , 2011 .

[41]  Jan Hensen,et al.  A new methodology for investigating the cost-optimality of energy retrofitting a building category , 2015 .

[42]  Ala Hasan,et al.  Applying a multi-objective optimization approach for Design of low-emission cost-effective dwellings , 2011 .

[43]  Mohamed El Mankibi,et al.  Development of a multicriteria tool for optimizing the renovation of buildings , 2011 .

[44]  C. Y. Zheng,et al.  A novel operation strategy for CCHP systems based on minimum distance , 2014 .

[45]  Thong Ngee Goh,et al.  A study of project selection and feature weighting for analogy based software cost estimation , 2009, J. Syst. Softw..

[46]  Gianluca D'Errico,et al.  Multi-objective optimization of internal combustion engine by means of 1D fluid-dynamic models , 2011 .

[47]  Fabio Polonara,et al.  Distributed generation and trigeneration: energy saving opportunities in Italian supermarket sector , 2009 .

[48]  Gerardo Maria Mauro,et al.  Design of the Building Envelope: A Novel Multi-Objective Approach for the Optimization of Energy Performance and Thermal Comfort , 2015 .

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

[50]  Christina J. Hopfe,et al.  Uncertainty and sensitivity analysis in building performance simulation for decision support and design optimization , 2009 .

[51]  Ferdinando Salata,et al.  Energy and reliability optimization of a system that combines daylighting and artificial sources. A case study carried out in academic buildings , 2016 .

[52]  Enrico Fabrizio,et al.  Reference buildings for cost optimal analysis: Method of definition and application , 2013 .

[53]  Jlm Jan Hensen,et al.  Uncertainty analysis in building performance simulation for design support , 2011 .

[54]  Pieter de Wilde,et al.  Identification of key factors for uncertainty in the prediction of the thermal performance of an office building under climate change , 2009 .

[55]  Philippe Rigo,et al.  A review on simulation-based optimization methods applied to building performance analysis , 2014 .

[56]  Evangelos Grigoroudis,et al.  A multi-objective decision model for the improvement of energy efficiency in buildings , 2010 .

[57]  Zhenjun Ma,et al.  Supervisory and optimal control of central chiller plants using simplified adaptive models and genetic algorithm , 2011 .

[58]  Gerardo Maria Mauro,et al.  Simulation-based model predictive control by the multi-objective optimization of building energy performance and thermal comfort , 2016 .

[59]  G. K. Alexis,et al.  A case study of a cogeneration system for a hospital in Greece. Economic and environmental impacts , 2013 .

[60]  T. Simpson,et al.  Comparative studies of metamodelling techniques under multiple modelling criteria , 2001 .

[61]  Janusz Bujak,et al.  Heat consumption for preparing domestic hot water in hospitals , 2010 .

[62]  Alfredo Gimelli,et al.  Optimization criteria for cogeneration systems: Multi-objective approach and application in an hospital facility , 2013 .

[63]  Taehoon Hong,et al.  A review on sustainable construction management strategies for monitoring, diagnosing, and retrofitting the building's dynamic energy performance: Focused on the operation and maintenance phase , 2015 .