Energy-use modelling for residential buildings in the metropolitan area of Gran Mendoza (AR)

Mutani, Guglielmina; Fontanive, Manuela; Arboit, Mariela Edith

doi:10.18280/ti-ijes.620204

Cited by 11 publications

(14 citation statements)

References 3 publications

(3 reference statements)

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“…The achievement of the best accuracy depends on the reliability of data and databases. In this study, to enhance pragmatism and ensure reproducibility, variables were obtained from institutional open-access databases [17][18][19], geographical and environmental data were calculated using well established procedure with GIS software [20,21], while electric consumption observations were obtained from the database provided by the Piedmont Region technical office, which collects energy data directly from the Distribution System Operators (DSOs) [22].…”

Section: Methodsmentioning

confidence: 99%

Statistical Data Analysis for Energy Communities

Mutani¹,

Santantonio²,

Tartaglia³

2020

TI-IJES

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The objectives of the European Energy transition entail an increasing use of electricity especially for residential sector. Member states are invited to promote energy policies that involve stakeholders directly. Energy Communities (EC) are intended as local institutions that could drive this change, creating local-scaled energy entities that cooperate to exchange energy. The purpose of this study is to investigate the energy consumption identifying a linear regression model to forecast electric energy demand at municipal scale, for residential end users. This work analyses electric consumption of 1,201 municipalities in Piedmont (north-west of Italy) evaluating the main energy-related variables. Information are obtained by online databases and georeferenced with GIS tool. The identified model evidences that the most influential variables are the population, the number of members per family, the education level, and the income. Regarding building features, the dwelling area and the number of occupied dwellings, the age of buildings and their maintenance condition. The statistical GIS-based methodology proposed in this study is replicable and can be applied to other contexts. A forecasting model to predict the amount of energy demand can support preliminary decision-making process defining the scale of ECs and their optimal configuration for balancing energy demand and local production.

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Section: Methodsmentioning

confidence: 99%

Statistical Data Analysis for Energy Communities

Mutani¹,

Santantonio²,

Tartaglia³

2020

TI-IJES

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“…The bottomup model requires exhaustive database with information at building level, as well as the energy consumption of each building. The top-down model uses aggregated information at territorial level [19][20][21][22][23]. Several tools were developed to facilitate the energy simulations of buildings in different urban areas: Geographic Information System (GIS), Energy Plus, CitySim Pro, UMI, and SimStadt.…”

Section: Tools For Urban Energy Analysesmentioning

confidence: 99%

Low-Energy Architecture for Sustainable Neighborhoods

Todeschi¹,

Beltramino²,

Jamous³

et al. 2021

TI-IJES

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Nowadays, energy consumption in buildings is one of the fundamental drivers to control greenhouse gas emissions and environmental impact. In fact, the air quality of urban environments can cause two main phenomena in metropolitan areas: urban heat island and climate changes. The aim of this work is to showcase how different building variables can impact the residential building’s space heating and cooling energy consumption. Buildings energy-related variables can be fundamental viewpoints to improve the energy performance of neighborhoods, especially in future urban planning. This work examines four neighborhoods in the city of Turin (IT): Arquata, Crocetta, Sacchi, and Olympic Village characterized by different morphologies and building typologies. In each neighborhood, residential building was grouped according to orientations and construction periods. A sensitivity analysis was applied by analysing six building variables: infiltration rate, window-to-wall ratio, and windows, walls, roofs, and floor thermal transmittances. The energy consumption for space heating and cooling of residential buildings and local climate conditions were investigated using CitySim Pro tool and ENVI-met. The challenge of this work is to identify the building variables that most influence energy consumption and to understand how to promote high-energy efficiency neighborhoods: the goal is to identify the “ideal” urban form with low consumption and good comfort conditions in outdoor urban environments. The results of this work show a significant connection between the energy consumption and the six analyzed building variables; however, this relationship also depends on the shape and orientation of the neighborhood.

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“…(2) Energy input Energy is an essential factor in production activities. In the related research on environmental efficiency, a large number of researchers have gradually considered energy as an input factor into the index system (Mutani et al, 2018;Vand et al, 2018). The rapid economic growth is inseparable from the massive consumption of energy, but the latter is also accompanied by the generation of a large number of pollutants.…”

Section: Data Descriptionmentioning

confidence: 99%

Measurement of Regional Environmental Efficiency in China Based on the Super-Efficiency SBM Model

Zhou¹

2019

Appl. Ecol. Env. Res.

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This paper measures the regional environmental efficiency in China by using the superefficiency slacks-based measure (SBM). It calculates the environmental efficiency in the presence and absence of the environmental pollution variable respectively, and compares the calculation results of the super-efficiency SBM model with the Banker & Charnes & Cooper's (BCC) model, one of the traditional data envelopment analysis (DEA) models. By analyzing the environmental efficiencies in all provinces, it analyzes the slack variables to determine in which direction each region should improve. The results show that the addition of the environmental pollution variable has resulted in significant declines in the efficiencies of provinces with DEA inefficiencies. Among the four regions, the eastern one has the highest environmental efficiency, the western region comes next and the northeastern and central regions are the last ones. There are also some economically developed provinces with low environmental efficiency, and different inefficient provinces need to be improved in different directions.

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Energy-use modelling for residential buildings in the metropolitan area of Gran Mendoza (AR)

Cited by 11 publications

References 3 publications

Statistical Data Analysis for Energy Communities

Statistical Data Analysis for Energy Communities

Low-Energy Architecture for Sustainable Neighborhoods

Measurement of Regional Environmental Efficiency in China Based on the Super-Efficiency SBM Model

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