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Home Energy Management Systems Adoption Scenarios: The Case of Italy

doi: 10.3390/en16134946
The 2030 zero-net emission target in the E.U. demands a significant improvement in the energy performance of the building stock. This study analyses the adoption of connected thermostats and Home energy-management system solutions (HEMS) as an effective means to tackle the residential energy footprint. It reviews the main features of HEMS systems in terms of technology, cross-study performances, and the obstacles to widespread adoption; the study adopts the case-study methodology to examine the impact on the Italian real estate stock at a regional level. A matrix of adoption scenarios assesses the potential benefits of global residential energy savings, weighted by local climatic variations, dimension, number of single dwellings, and average primary energy reduction per household. Results demonstrate that all adoption scenarios dramatically reduce residential energy consumption, outperforming the E.U. targets for Italy by 2030.
Technology, T, smart building, HEMS; energy consumption; smart building; energy policy; Italian government, energy consumption, Italian government, energy policy, HEMS
Technology, T, smart building, HEMS; energy consumption; smart building; energy policy; Italian government, energy consumption, Italian government, energy policy, HEMS
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).11 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 10% influence This indicator reflects the overall/total impact of an article in the research community at large, based on the underlying citation network (diachronically).Average impulse This indicator reflects the initial momentum of an article directly after its publication, based on the underlying citation network.Top 10%
