Assessing household energy uses: An online interactive tool dedicated to citizens and local stakeholders

Marique, Anne-Françoise; Cuvellier, Simon; Herde, André De; Reiter, Sigrid

doi:10.1016/j.enbuild.2017.06.075

Cited by 8 publications

(8 citation statements)

References 19 publications

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“…The distribution of the statistical sectors according to the net density of dwellings per hectare in Wallonia is expressed in the form of a Gaussian curve. The urban density classes were assessed using Jenks' natural breaks classification and the SOLEN method [56,57].…”

Section: Methods Used To Assess Urban Density Classes In Walloniamentioning

confidence: 99%

“…In this study, we applied the renovation rate to all the dwellings distributed in the different neighbourhoods that we studied. We estimated that a major renovation allows the renovated building to meet the 'low energy' criterion, with heating energy consumption of 30 kWh/m 2 /year [57]. A light renovation leads the building to a consumption half-way between that of the 'low energy' criterion and that currently observed.…”

Section: Application Of Renovation Scenariosmentioning

confidence: 99%

“…By applying this scenario to our neighbourhoods, we obtain a consumption reduction in fossil fuels of more than 4612 GWh/year. This scenario needs to be nuanced by considering a roof correction factor [56,57], taking into account the impact of shading between buildings, according to the type of neighbourhood built density. Roof correction factor was assessed and is shown in Table 6.…”

Section: Application Of Photovoltaic Panel Scenariomentioning

confidence: 99%

“…-Energy consumption of all buildings for the reference year, -Solar energy production from 20 m 2 of PV panels on all the building rooftops (without considering the effects of shading between buildings), -Remaining fossil energy consumption after the use of solar energy produced (without considering the impact of shading between buildings), -Roof correction factor to apply on solar energy production taking into account the impact of shading between buildings, based on their built density [56,57]. -Solar energy production with roof correction factor, considering the impact of shading between buildings, based on their built density [56,57]. -Remaining fossil energy consumption after using the solar energy produced, considering the impact of shading between buildings, based on the built density [56,57].…”

Section: Application Of Photovoltaic Panel Scenariomentioning

confidence: 99%

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Towards nearly zero-energy residential neighbourhoods in the European Union: A case study

Nematchoua

Nishimwe

Reiter

2021

Renewable and Sustainable Energy Reviews

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The European Union (EU) aims to establish a guideline that requires all new buildings to comply with nearly zero-energy buildings (NZEB) by 2030. This decision involves new technologies based on concepts that meet international standards. This research aims to review the literature on 'net zero-energy building' and analyses the possibility of applying this research on nine statistically representative neighbourhoods of the building stock'in Belgium, depending on the built density. All the areas, grouped into four categories (urban, peri-urban, suburban, and rural neighbourhoods), were used for current energy consumption analysis and to evaluate prospective scenarios based on four major challenges, namely climate change, building renovations, photovoltaic panels, and sustainable mobility. In addition, a new approach combining several scenarios to further improve energy needs at the neighbourhood scale is also highlighted. The nine different types of neighbourhoods studied are commonly found in different countries across the EU. The average reduction in energy consumption of neighbourhoods (buildings + daily mobility) in 2040 (compared to reference year 2012) will likely reach 5.69% attributable to a 20% reduction in distances travelled, 6.48% to climate change, 12.95% to the current annual buildings renovation rate, 18.76%-100% electric cars, 22.26% for doubling the current buildings renovation rate, 31.62% and 63.25% to a light or heavy renovation of the entire building stock, respectively. Moreover, installing 20 m 2 of solar panels on the rooftops of each residential building would produce renewable energy equivalent to 6.53% of the current global energy consumption. Finally, the results show that more than 90% of current energy consumption can be reduced at the neighbourhood scale (buildings + daily mobility) by combining a heavy renovation of all the buildings, electric vehicles, and photovoltaic panels. This scenario allows reaching the 'nearly zero-energy' target at the neighbourhood scale.

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Section: Methods Used To Assess Urban Density Classes In Walloniamentioning

confidence: 99%

Section: Application Of Renovation Scenariosmentioning

confidence: 99%

Section: Application Of Photovoltaic Panel Scenariomentioning

confidence: 99%

Section: Application Of Photovoltaic Panel Scenariomentioning

confidence: 99%

See 2 more Smart Citations

Towards nearly zero-energy residential neighbourhoods in the European Union: A case study

Nematchoua

Nishimwe

Reiter

2021

Renewable and Sustainable Energy Reviews

Self Cite

View full text Add to dashboard Cite

show abstract

“…sub-hourly, hourly, daily, monthly, yearly) (Aljumaily et al 2017;Calabrese et al 2015; Wang and Taylor 82 2015). Moreover, numerous interdependencies exist between various urban systems including building-83 transportation (Marique et al 2017), building-human (Langevin et al 2015), building-vegetation (Perini 84 and Magliocco 2014), transportation-land and environment-human-transportation interdependencies 85 (Raymond et al 2013). As a result, an urban data integration framework must first and foremost be able to 86 reconcile the spatial, temporal and typological heterogeneity that characterizes urban data streams in order 87 to extract insights across urban systems valuable for holistic municipal planning and decision-making.…”

Section: Related Workmentioning

confidence: 99%

Urban Data Integration Using Proximity Relationship Learning for Design, Management, and Operations of Sustainable Urban Systems

Gupta

Yang

Jain

2019

J. Comput. Civ. Eng.

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10The world is rapidly urbanizing with 66% of the world's population expected to reside in cities by 2050. 11 This massive influx of new urban citizens is putting enormous pressure on city systems and bringing forth 12 challenges at the intersection of urban infrastructure, governance and the environment. As a result, 13 researchers and practitioners have turned to new advanced sensing and data analytics developed under the 14 burgeoning "smart city" movement to improve the design, management and operations of urban systems. 15However, data emerging from urban systems has been challenging to integrate, organize and analyze due 16 to their natural spatial, temporal and typological heterogeneity. In this paper, an Urban Data Integration 17 (UDI) framework is introduced that is capable of integrating heterogeneous urban data. The proposed UDI 18 framework is extensible to multiple types of urban systems, scalable to the growing amount and quickly 19 changing urban data streams and interpretable enough to help inform municipal decision-making. The UDI 20 framework utilizes a series of novel proximity relationship learning algorithms to automatically reconstruct 21 urban data in a graph database. The merits, applicability and efficacy of the proposed framework is 22 demonstrated by validating and testing it on data from a mid-size city in the United States and by 23 benchmarking its interpretability and computational performance for a typical urban analytics scenario 24 against current practice (i.e., a relational database). Results indicate that the UDI framework provides easier 25 and more computationally efficient exploration and querying of urban data and in turn can enable new 26 computational approaches to urban system design, management and operations. 27Keyword: data integration, graph database, proximity learning, smart city, urban data 28The heterogeneity of urban data and the nascent field of urban analytics both point towards the need for 53 integrating urban data early and often. For example, if new data becomes available on the air quality along 54 a main traffic corridor, a municipal official could want to map this new information to existing data sources 55 on related systems (e.g., traffic, roads), re-run analytical queries to understand mutual influences and then 56 take appropriate actions. Maintaining a high-level of interpretability is vital during the integration process 57 as the goal is to support urban design and operational decisions by municipal officials, policy-makers and 58 engineers. As a result, a useful urban data integration framework must be extensible to multiple urban 59 systems (and not system specific), scalable to the growing amounts of quickly changing urban data streams 60 and interpretable such that it can inform decision-making. 61In this paper, an Urban Data Integration (UDI) framework is introduced that integrates heterogeneous urban 62 data while maintaining extensibility, scalability and interpretability. The proposed UDI framework utilizes 63 a series of novel proxi...

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On the use of questionnaire in residential buildings. A review of collected data, methodologies and objectives

Carpino

Mora

Simone

2019

Energy and Buildings

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Assessing household energy uses: An online interactive tool dedicated to citizens and local stakeholders

Cited by 8 publications

References 19 publications

Towards nearly zero-energy residential neighbourhoods in the European Union: A case study

Towards nearly zero-energy residential neighbourhoods in the European Union: A case study

Urban Data Integration Using Proximity Relationship Learning for Design, Management, and Operations of Sustainable Urban Systems

On the use of questionnaire in residential buildings. A review of collected data, methodologies and objectives

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