Modelling energy retrofit using household archetypes

Ben, Hui; Steemers, Koen

doi:10.1016/j.enbuild.2020.110224

Cited by 18 publications

(9 citation statements)

References 49 publications

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“…Using data from the survey, simulate the software's default data and Data published in ASHRAE and CIBSE guidelines, Hui Ben etc. developed five archetypes: active spenders, conscious occupiers, average users, conservers and inactive users based on comfort, behavior, energy use and household characteristics, to represent the building stocks in Cambridge [47]. The energy saving potential of archetypes building for 8 types of retrofit measure were simulated using energy consumption simulation software IES-VE.…”

Section: Archetypes Aggregation Approachmentioning

confidence: 99%

Energy-saving potential prediction models for large-scale building: A state-of-the-art review

Yang

Liu

Zou

et al. 2022

Renewable and Sustainable Energy Reviews

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Section: Archetypes Aggregation Approachmentioning

confidence: 99%

Energy-saving potential prediction models for large-scale building: A state-of-the-art review

Yang

Liu

Zou

et al. 2022

Renewable and Sustainable Energy Reviews

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“…Urban-scale energy models (USEMs) are tools that are able to evaluate the distribution of the energy consumption of buildings at a city scale by applying energy saving scenarios (Sebi et al, 2019;Sola et al, 2018). Through an evaluation of the existing EE policies, it is possible to assess the effect of different energy retrofitting measures on improving the EP of buildings (Ben & Steemers, 2020;Guelpa et al, 2018). Since USEMs take into consideration the real characteristics of an urban environment, they can be used to support territorial energy planning, through the promotion of a sustainable development of cities towards energy transition (Sola et al, 2019).…”

Section: State Of the Artmentioning

confidence: 99%

GIS-based urban energy modelling and energy efficiency scenarios using the energy performance certificate database

Mutani

Todeschi

2021

Energy Efficiency

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The EU building stock is 97% not energy efficient and the promotion of energy retrofitting strategies is a key way of reducing energy consumptions and greenhouse gas emission. In order to improve the energy performance of buildings, the European Union released the Energy Performance of Buildings and the Energy Efficiency Directives. The certification of the energy performance of a building is a central element of these Directives to monitor and promote energy performance improvements in buildings, with the aim of increasing their energy efficiency level, thereby reducing greenhouse gas emissions. This work evaluates the energy performance of existing residential buildings using the energy performance certificate database and identifies the more effective retrofitting interventions by applying an urban-scale energy model. The novelty of this study is that a new retrofitting database is created to improve the results of a building energy model at urban scale taking into account the real characteristics of the built environment. The here presented GIS-based monthly engineering model is flexible and easily applicable to different contexts, and was used to investigate energy efficiency scenarios by evaluating their effects of city scale. An urban energy atlas was designed for an Italian city, Turin, as a decision-making platform for policy makers and citizens. This energy platform can give information on energy consumption, production and productivity potential, but also on energy retrofitting scenarios. The results of this work show that it is possible to obtain energy savings for space heating of 79,064 MWh/year for the residential buildings connected to the district heating network in the city of Turin; these interventions refer mainly to thermal insulation of buildings envelope with windows replacement and allow a reduction in greenhouse gas emissions of 12,097 tonCO2eq/year.

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“…By defining them as probability distributions, the unknown or uncertain archetypes parameters result in a combined solution to the lack of variety of archetypes [22]. Moreover, Ben and Steemers [23] explored the extension of building archetypes to households to overcome the significant variations found in occupant behavior. Findings from statistical analyses of behavioral patterns resulted in five household archetypes: active spenders, conscious occupiers, average users, conservers and inactive users [24].…”

Section: Introductionmentioning

confidence: 99%

Building Stock Energy Model: Towards a Stochastic Approach

Panão

Penas²

2022

Energies

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This work uses the outcome of a computational tool that performs Energy Performance Certification (EPC) data processing and transforms raw data into comparable data. Multi-correlation among variables results in probability distributions for the most relevant form and fabric building parameters. The model consistently predicts the distributions for heating and cooling energy needs for the Lisbon Metropolitan Area, with an error below 7% for the first, second and third quartiles. Differences in the energy needs estimation are below 6% when comparing the seasonal steady-state with the resistance-capacitance (RC) model, which proved to be a robust alternative algorithm capable of modeling hourly user profiles. The RC model calculates electricity consumption for actual, adequate, and minimum thermal comfort scenarios corresponding to different user profiles. The actual scenario, built from statistics and a previous survey, defines a reference to evaluate other scenarios for the mean electricity consumption for space heating and cooling in the building units with those systems. The results show that the actual mean electricity consumption for heating (610 kWh/y) is slightly above the minimum (512 kWh/y), with 37% of building units potentially under heated. The electricity consumption (108 kWh/y) for cooling is below the minimum (129 kWh/y).

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Modelling energy retrofit using household archetypes

Cited by 18 publications

References 49 publications

Energy-saving potential prediction models for large-scale building: A state-of-the-art review

Energy-saving potential prediction models for large-scale building: A state-of-the-art review

GIS-based urban energy modelling and energy efficiency scenarios using the energy performance certificate database

Building Stock Energy Model: Towards a Stochastic Approach

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