Urban Residential Buildings’ Energy Consumption Pattern and Efficiency

Ahady, Shambalid; Dev, Nirendra; Mandal, Anubha

doi:10.1007/s40996-022-00848-3

Cited by 3 publications

(2 citation statements)

References 20 publications

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“…The theoretical application of this theory is in explaining how citizens choose suitable housing. They determine the accommodation based on action and thought using accurate spatial data to respond to their family's feelings and physiological needs and improve and efficiency of housing (Ahady, 2022). Despite the extensive body of research on urban housing markets, a notable gap remains in the application of advanced data mining techniques to the Tehran housing market.…”

Section: Literature Reviewmentioning

confidence: 99%

Advanced modeling of housing locations in the city of Tehran using machine learning and data mining techniques

Pilehvar,

Ghasemi

2024

Humanit Soc Sci Commun

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This research delves into the intricate dynamics of housing location in the bustling metropolis of Tehran. It aims to gain a deeper understanding of the factors influencing housing prices across the city. Employing a descriptive-analytical method, the study utilizes the Python programming language and its libraries, along with various regression models, to analyze a comprehensive dataset of 8000 villas and apartments spread across 22 districts and 317 areas. Data obtained from official sources are used to examine the correlation between housing prices and nine key determinants. The findings reveal strong positive correlations between the total value of the houses and several factors: surface area (80%), neighborhood location (75%), presence of an elevator (44%), presence of a parking lot (43%), and year of construction (26%), these demonstrate the importance of area and neighborhood. Conversely, the distinct number shows an inverse correlation (−41%) which means the higher the distinct number is, the lower the total value will be. In its final stage, the study employs cross-validation to evaluate the performance of various learning models, achieving a maximum accuracy of 85%. The research concludes by presenting a new formulation and modeling approach for determining the total value of housing, showcasing its originality and contributions to the field.

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Section: Literature Reviewmentioning

confidence: 99%

Advanced modeling of housing locations in the city of Tehran using machine learning and data mining techniques

Pilehvar,

Ghasemi

2024

Humanit Soc Sci Commun

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“…Moreover, other authors show that commercial energy use in total household energy consumption in rural areas is increasing in some countries. Additionally, they show that energy consumption differs by building type, and the type of energy transition solutions varies based on the different household structures and where they are locationed [12][13][14][15]. The literature also analyses fuel consumption patterns [16,17] and the adaptation of gaseous fuels and electricity to everyday cooking activities as part of clean energy adoption [18][19][20][21].…”

Section: Literature Review 21 Household Energy Transitionmentioning

confidence: 99%

Households’ Energy Transformation in the Face of the Energy Crisis

2023

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The purpose of conducted research was to recognize factors determining households’ Energy transition and barriers that slow that process. Energy transition itself, understood as a shift in the structure of fuels used in energy production and technological changes related to its use, are key elements of shaping the economy. It was determined to what extent existing household renewable energy installations meet the energy needs of their residents and what factors encourage their installation. In addition, barriers limiting energy transition as perceived by household members were identified. The research used data from the EUROSTAT and the results of surveys conducted using the CAWI (Computer-Assisted Web Interview) and PAPI (Paper and Pencil Interview) techniques among households in Poland. The methods of descriptive statistics, the chi-square test of independence, the Kruskal–Wallis ANOVA test, the Mann–Whitney U test and logistic regression were used to analyze the research results. The analyses show that fossil fuels dominate in energy production in Poland. The share of renewable energy sources in the gross final energy consumption in 2020 was 16.1%. Their structure is dominated by photovoltaic installations with a share of 52%. Further increase in energy prices and fears of interruptions in energy supplies will favor the further increase in the number of installations for renewable energy sources. According to the respondents, the energy efficiency has a significant impact on the quality of life and environment, but the main barriers to its development include financial constraints of households.

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