A GIS-statistical approach for assessing built environment energy use at urban scale

Moghadam, Sara Torabi; Toniolo, Jacopo; Mutani, Guglielmina; Lombardi, Patrizia

doi:10.1016/j.scs.2017.10.002

Cited by 98 publications

(35 citation statements)

References 41 publications

(41 reference statements)

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“…building shape, age, and occupant behavior) 10 . They are also able to take into account socioeconomic effects in the equations 11 . They calculate reliable consumption based on the available information on the current status of buildings.…”

Section: /31mentioning

confidence: 99%

“…They calculate reliable consumption based on the available information on the current status of buildings. However, due to their strong dependency on available historical consumption data, these methods are restricted to predict the impact of new technology options and energy saving potential after applying refurbishment measures 11 .…”

Section: /31mentioning

confidence: 99%

“…A statistical methodology was previously developed to estimate the heating space energy consumption of residential stocks over an entire city 11 . The model was based on a MLR method and it has been validated through cross-validation approach for the entire city.…”

Section: Statistical Heating Energy Consumption Modelingmentioning

confidence: 99%

“…The model was applied for the housing stock of the city. A deeper detailed description of data inputs and applied methodology are described in Torabi Moghadam et al 11 . In this previous study, we updated the GIS energy-database with real measured district heating data and estimated heating data to create an energy consumption map and to understand the current status of the whole city.…”

Section: Statistical Heating Energy Consumption Modelingmentioning

confidence: 99%

“…In the case of Settimo Torinese housing stock 11 , the main influencing parameters were defined as: the period of construction (x pc ), the type of ground floor(x g f ), the perimeter (x p ), the number of floors (x n f ), the heated volume (x v ), and the occupied buildings factor (x o f ). The terms β refers to the regression coefficients estimated by using the Ordinary Least Squares (OLS) method and ε is a remaining errors.…”

Section: /31mentioning

confidence: 99%

See 4 more Smart Citations

A new clustering and visualization method to evaluate urban energy planning scenarios

Moghadam¹,

Coccolo²,

Mutani³

et al. 2018

Preprint

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The spatial visualization is a very useful tool to help decision-makers in the urban planning process to create future energy transition strategies, implementing energy efficiency and renewable energy technologies in the context of sustainable cities. Statistical methods are often used to understand the driving parameters of energy consumption but rarely used to evaluate future urban renovation scenarios. Simulating whole cities using energy demand softwares can be very extensive in terms of computer resources and data collection. A new methodology, using city archetypes is proposed, here, to simulate the energy consumption of urban areas including urban energy planning scenarios. The objective of this paper is to present an innovative solution for the computation and visualization of energy saving at the city scale. The energy demand of cities, as well as the micro-climatic conditions, are calculated by using a simplified 3D model designed as function of the city urban geometrical and physical characteristics. Data are extracted from a GIS database that was used in a previous study. In this paper, we showed how the number of buildings to be simulated can be drastically reduced without affecting the accuracy of the results. This model is then used to evaluate the influence of two set of renovation solutions. The energy consumption are then integrated back in the GIS to identify the areas in the city where refurbishment works are needed more rapidly. The city of Settimo Torinese (Italy) is used as a demonstrator for the proposed methodology, which can be applied to all cities worldwide with limited amount of information. KeywordsBuilding Energy Consumption; Geographical Information System; Statistical Models; Deterministic Models; Urban Energy Planning. Highlights• We use a large building energy 2D-GIS database to predict an energy consumption by statistical methodology.• A new engineering methodology, using 3D city archetype, is proposed to estimate energy savings 1 for building retrofits.• We compared the results using monitored data and with energy consumption from a statistical method.• The proposed spatial decision support system is developed to visualize the refurbishment scenarios for decision-making processes.

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Section: /31mentioning

confidence: 99%

Section: /31mentioning

confidence: 99%

Section: Statistical Heating Energy Consumption Modelingmentioning

confidence: 99%

Section: Statistical Heating Energy Consumption Modelingmentioning

confidence: 99%

Section: /31mentioning

confidence: 99%

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A new clustering and visualization method to evaluate urban energy planning scenarios

Moghadam¹,

Coccolo²,

Mutani³

et al. 2018

Preprint

Self Cite

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Understanding Social Dynamics in Urban Building and Transportation Energy Behavior

Abbasabadi

2024

Artificial Intelligence in Performance‐Driven Design

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Spatial assessment on influence of land use and population density in the achievement score of sustainable development target 11.1

Baskaran¹,

Velkennedy²

2023

Sustainable Development

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The United Nations (U.N.) has declared the present decade a “decade of action” to accelerate the phase of 17 Sustainable Development Goals (SDGs) endorsed in 2015. These 17 goals are intended to protect the earth's environment, eradicate poverty and inequality, and guarantee that everyone lives in peace, harmony, and prosperity. Target 11.1 for safe and affordable housing from SDG 11: Sustainable cities and community is a crucial target for most developing nations. This target addresses the slums and informal settlements, which indicate inequalities in the urbanized city. The proportion of the population living in slum dwellings and informal settlements was the indicator to measure the achievement level of the target. The study area to assess the target is Tirunelveli City municipal corporation from southern India. The field survey was conducted in all 55 wards to locate and count the population of slums and informal settlements and to map the spatial variation of target scores among the wards. The spatial assessment was performed by comparing the attributes of population density and land use classes (water, built area, and bare land) with the achievement score of target 11.1 for each ward. With an overall score of 90.08, this city has no significant relation with population density (R2 = 0.07) and relevant land use classes (R2 = 0.05) at the ward level. The spatial tool, weighted mean centre, has concluded that slum settlements appeared randomly and dispersed throughout Tirunelveli without any pattern.

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A GIS-statistical approach for assessing built environment energy use at urban scale

Cited by 98 publications

References 41 publications

A new clustering and visualization method to evaluate urban energy planning scenarios

A new clustering and visualization method to evaluate urban energy planning scenarios

Understanding Social Dynamics in Urban Building and Transportation Energy Behavior

Spatial assessment on influence of land use and population density in the achievement score of sustainable development target 11.1

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