It is widely recognized that the building sector largely contributes to the total European energy consumption with a 40% influence on the total assessed energy uses. To this regard the EPBD recast Directive promotes nearly zero energy buildings (nZEB) for the public and the private sector as a mandatory requirement by 2020. Given the low energy efficiency of old buildings, concerns about the state of the existing building stock should be seriously considered as most of the energy consumption is attributable to the existing buildings. Additionally, residential buildings are often seen as long-term assets, setting thus a low replacement rate, approximately 1% per year in Europe, of old buildings by new ones. To this regard, larger energy savings can be achieved with the energy retrofitting of the existing building stock, rather than with the construction of relatively small proportion of new high performing buildings. Therefore, the refurbishment of the existing building stock has to be primarily planned and accomplished in order to achieve a timely reduction on the buildings energy consumption. Concerning this, the EPBD recast, as policy driver for reducing European energy use in buildings, has been representing the first and main legislative reference. According to it Member States must ensure that minimum energy performance requirements are set with a view of achieving at least cost-optimal levels for buildings, building units and building elements" by means of a comparative methodology framework applied to new constructions and existing buildings undergoing major renovations. The methodology specifies how to compare energy efficiency measures in relation to their energy performance and to the cost attributed to their implementation, and how to apply these to selected reference buildings with the aim of identifying cost-optimal levels of minimum energy performance requirements. A cost optimal level is defined as the energy performance level, which leads to the lowest cost during the estimated economic lifecycle of the building. A measure is considered cost-effective when the cost of implementation is lower than the achievable benefits, during the expected life of the measure. This type of analysis allows defining energy renovation scenario based on their energy and economic optimum. Within the complex scenario described above, this Ph.D. thesis aims to provide a scalable methodology for the definition of energy retrofit scenarios to be applied to existing buildings, based on the use of dynamic building simulation models. The methodology targets the existing building stock given the large energy savings that can be achieved from existing buildings. It builds on an energy and economic assessment of energy efficiency measures applied to different building typologies. The energy and economic assessment are respectively carried out by means of dynamic building simulation and a cost-optimality approach. The cost optimal analysis was chosen for the aim of this study for its systematic approach in defining energy retrofit interventions based on their energy and economic optimum. The term "scalable" is used for defining the methodology as the studied energy retrofit scenarios can vary depending on the "scale" of the study. Two main scales of buildings can be distinguished: building stock or single buildings. When retrofit interventions are studied to be applied to wide portion of the building stock, as for example at national level, representative building models are used. They correspond to reference buildings representative of a certain building typology, construction age and geographic location. Within this thesis, a methodology for their definition was defined and various reference buildings for the Italian context were created. On the contrary, when it is necessary to study specific and customized retrofit measures, a single existing building is modelled. In this case, compared to the case of the reference buildings, larger quantity of data and a higher degree of detail are necessary. These building models are customized based on the existing buildings characterization (e.g. building envelope, system, etc) and when applicable, based also on data from monitoring. To this regards, when detailed information about the building real operation from monitoring is available, the building model need to be calibrated based on measured data. For a model to be calibrated, the building energy consumption predicted by the simulation program, has to match the consumption measured from monitoring. Calibrated models can be used for comparing the baseline situation of the building (calibrated and not retrofitted) with other simulation results relative to the application of building renovation interventions. To this regard, within this Ph.D. thesis, a literature review on the most common calibration techniques currently in use for the calibration of building models was conducted. Additionally two case studies were calibrated by means of two different approaches: a trial and error approach and an optimization-based calibration. For both scale of buildings (building stock and single buildings), dynamic building simulation was employed for the energy assessment. Building simulation application has expanded since mid-‘70s building simulation as an attempt to emulate reality. To date, it is much more common to employ building simulation in post construction or advanced building design phases rather than in early phases. In particular building simulation is frequently used for the prediction of energy savings by assessing energy retrofit interventions on existing buildings. To this regard, given its wide application and the high level of detail of the analysis performed (dynamic analysis), building simulation was chosen within this thesis, as a tool for the energy assessment of buildings and of the relative energy renovation interventions. Finally, the economic assessment of the energy retrofit measures was carried out by means of the cost optimal methodology, as defined by the EPBD recast. The methodology allowed defining energy renovation interventions based on their economical and energy optimum. The Directive requires to define different packages of energy efficiency measures, which can be applied to reference representative buildings but also to single and existing building for energy and economical assessments. The energy assessment of a building can be carried out with analytical or simplified methods, but dynamic building simulation is strongly suggested, as performed within this thesis. For the economic assessment, the global cost method was employed based on the calculation method of the Standard EN 15459 as advised by the EBPD. The global cost method considers, for each energy efficiency measure, the initial investment, the sum of the annual costs for every year (including energy costs) and the final value, all with reference to the starting year of the calculation period. In order to define different energy retrofit solution and set the minimum energy performance requirements, within the Ph.D. thesis, the cost optimal approach was applied to both the considered scale of buildings: to the building stock scale with three reference buildings, and to the single buildings scale with two calibrated buildings. A set of energy efficiency measures was defined and applied to the case studies for evaluating the financial and energy performance gap between the cost-optimal solutions and nZEB levels, respectively. For the building stock, different energy retrofit solutions are defined as final outcomes. Given the use of representative models (reference buildings), the retrofit solutions can be replicated to several buildings, among the same building typology. In this sense, different energy retrofit solutions can be established. On the other hand, for single buildings, the energy retrofit solution studied is specific and customized barely to the analyzed case study
[1]
Gerardo Maria Mauro,et al.
A new methodology for cost-optimal analysis by means of the multi-objective optimization of building energy performance
,
2015
.
[2]
A. K. Nicholls,et al.
Residential and commercial buildings data book: Third edition
,
1988
.
[3]
J.L.M. Hensen.
Energy simulation in building design
,
1992
.
[4]
Enrico Fabrizio,et al.
Livelli di prestazione energetica ottimali per edifici a energia quasi zero: il caso di un edificio multifamiliare
,
2012
.
[5]
Marco Vaudetti.
Edilizia per il commercio : punti vendita, concept store, grandi magazzini, centri commerciali, temporary store
,
2007
.
[6]
Veit Bürger.
Overview and assessment of new and innovative integrated policy sets that aim at the nZEB standard
,
2013
.
[7]
Jlm Jan Hensen,et al.
Overview of HVAC system simulation
,
2010
.
[8]
European Commission,et al.
Guidelines accompanying Commission Delegated Regulation (EU) No 244/2012 of 16 January 2012 supplementing Directive 2010/31/EU of the European Parliament and of the Council on the energy performance of buildings by establishing a comparative methodology framework for calculating cost-optimal levels
,
2012
.
[9]
David E. Claridge,et al.
Manual of Procedures for Calibrating Simulations of Building Systems
,
2003
.
[10]
Targo Kalamees,et al.
Cost optimal and nearly zero (nZEB) energy performance calculations for residential buildings with R
,
2011
.
[11]
Luca Rollino,et al.
Edifici tipo, indici di benchmark di consumo per tipologie di edificio, ad uso scolastico (medie superiori e istituti tecnici) applicabilità di tecnologie innovative nei diversi climi italiani
,
2010
.
[12]
Thomas Boermans,et al.
Cost optimal building performance r equirements Calculation methodology for reporting on national energy performance requirements on the basis of cost optimality within the framework of the EPBD
,
2011
.
[13]
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
.
[14]
T. Agami Reddy,et al.
Calibrating Detailed Building Energy Simulation Programs with Measured Data—Part I: General Methodology (RP-1051)
,
2007
.
[15]
Marco Filippi,et al.
Dynamic simulation of BACS (Building Automation and Control Systems) for the energy retrofitting of a secondary school
,
2013
.
[16]
Shady Attia,et al.
Principles for nearly Zero-energy Buildings: Paving the way for effective implementation of policy requirements
,
2011
.
[17]
Ergo Pikas,et al.
Cost optimal and nearly zero energy building solutions for office buildings
,
2014
.
[18]
Bing Liu,et al.
U.S. Department of Energy Commercial Reference Building Models of the National Building Stock
,
2011
.
[19]
Christoph F. Reinhart,et al.
Lightswitch-2002: a model for manual and automated control of electric lighting and blinds
,
2004
.
[20]
T. Agami Reddy,et al.
Literature review on calibration of building energy simulation programs : Uses, problems, procedures, uncertainty, and tools
,
2006
.
[21]
REGULATION (EU) No 1291/2013 OF THE EUROPEAN PARLIAMENT AND OF THE COUNCIL of 11 December 2013 establishing Horizon 2020 - the Framework Programme for Research and Innovation (2014-2020) and repealing Decision No 1982/2006/EC
,
2013
.
[22]
Ardeshir Mahdavi,et al.
A TWO-STAGED SIMULATION MODEL CALIBRATION APPROACH TO VIRTUAL SENSORS FOR BUILDING PERFORMANCE DATA
,
2013
.
[23]
J A Clarke,et al.
AN APPROACH TO THE CALIBRATION OF BUILDING ENERGY SIMULATION MODELS
,
1993
.
[24]
Yeonsook Heo,et al.
Calibration of building energy models for retrofit analysis under uncertainty
,
2012
.
[25]
Yeonsook Heo,et al.
Bayesian calibration of building energy models for energy retrofit decision-making under uncertainty
,
2011
.
[26]
Giuliano Dall'O',et al.
A methodology for evaluating the potential energy savings of retrofitting residential building stocks
,
2012
.
[27]
Lin Fu,et al.
Dynamic simulation of space heating systems with radiators controlled by TRVs in buildings
,
2008
.
[28]
Stewart Robinson,et al.
Conceptual modelling for simulation Part I: definition and requirements
,
2008,
J. Oper. Res. Soc..
[29]
Tobias Loga,et al.
Implementing the cost-optimal methodology in EU countries: Lessons learned from three case studies
,
2013
.
[30]
Cristina Becchio,et al.
The Influence of Energy Targets and Economic Concerns in Design Strategies for a Residential Nearly-Zero Energy Building
,
2014
.
[31]
Simon Johannes Bley,et al.
Smarter, greener, more inclusive? Indicators to support the Europe 2020 strategy - 2017 edition
,
2017
.
[32]
Rune Vinther Andersen,et al.
Influence of occupant's heating set-point preferences on indoor environmental quality and heating demand in residential buildings
,
2013,
HVAC&R Research.
[33]
Ilaria Ballarini,et al.
Assessment of Cost-optimal Energy Performance Requirements for the Italian Residential Building Stock
,
2014
.
[34]
Andrea Saltelli,et al.
From screening to quantitative sensitivity analysis. A unified approach
,
2011,
Comput. Phys. Commun..
[35]
P. Mazzei,et al.
Impianti di climatizzazione con deumidificazione chimica per supermercati
,
2004
.
[36]
Satish Kumar.
International performance measurement and verification protocol Volume III: Concepts and options for determining energy savings in new construction
,
2003
.
[37]
Enrico Fabrizio,et al.
Italian benchmark building models: the office building
,
2011
.
[38]
Mark Stetz,et al.
M&v guidelines: measurement and verification for federal energy projects, version 2.2
,
2000
.
[39]
Stéphane Bertagnolio.
Evidence-Based Model Calibration for Efficient Building Energy Services
,
2012
.
[40]
Jakob Stoustrup,et al.
Stability performance dilemma in hydronic radiators with TRV
,
2011,
2011 IEEE International Conference on Control Applications (CCA).
[41]
A. Zerrin Yılmaz,et al.
Adaptation of the cost optimal level calculation method of Directive 2010/31/EU considering the influence of Turkish national factors
,
2014
.
[42]
Communication from the European commission to the council, the European parliament, the economic and social committee and committee of the regions
,
2002
.
[43]
Enrico Fabrizio,et al.
A simulation-based optimization method for cost-optimal analysis of nearly Zero Energy Buildings
,
2014
.
[44]
Chiara Martini,et al.
Le detrazioni fiscali del 55-65% per la riqualificazione energetica del patrimonio edilizio esistente 2013
,
2014
.