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An analysis methodology for large-scale deep energy retrofits of existing building stocks: Case study of the Italian office building

Abstract This paper proposes an analysis methodology that can be applied by governments and local administrations to evaluate energy retrofit programs that involve entire building categories. Through a simulation based approach, that combines detailed energy modelling and algorithmic optimization, the analysis methodology is aimed to assess the magnitude of achievable energy savings for various retrofit levels along a cost-optimal path towards achieving net zero energy operation conditions. The proposed approach allows to identify the most cost-effective energy efficiency measures (EEMs) for building retrofits and their required investment costs. The retrofit potential benefits of the Italian office buildings stock is used as a case study to illustrate the application of the proposed analysis methodology. The results of the evaluation analysis for the Italian office building stock indicated a potential primary energy savings of 54% (i.e., 8000 GWh), when retrofitted through optimal combinations of EEMs and rooftop integrated photovoltaics. The optimal set of EEMs showed strong dependency on building typology and climate. In general, the cost-optimal combinations of EEMs and rooftop PV systems did not allow to reach net-zero energy operation conditions for any Italian office building type and climatic zone.
- University of Colorado Boulder United States
- Free University of Bozen-Bolzano Italy
citations This is an alternative to the "Influence" indicator, which also reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).93 popularity This indicator reflects the "current" impact/attention (the "hype") of an article in the research community at large, based on the underlying citation network.Top 1% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Top 10% impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 1%
