Motivation, preference, socioeconomic, and building features: New paradigm of analyzing electricity consumption in residential buildings

Building and Environment, Journal Year: 2022, Volume and Issue: 219, P. 109177 - 109177

Published: May 12, 2022

In strategic energy planning, human-oriented factors are uncertain and lead to unpredictable challenges. Thus, decision-makers must contextualize the target society address these uncertainties. More precisely, uncertainties performance gaps between assumed actual sustainability outcomes. This study proposed a new framework that considers vital elements, including occupant motivation, preference, socioeconomic characteristics, building features (MPSEB). To utilize this model, thorough face-to-face survey questionnaire was administered measure elements. explored how elements affect patterns of residential consumption in region with numerous expat communities various ethnic cultural backgrounds. particular, investigated behaviors human-building interactions among residents Qatar by collecting empirical evidence conducting subsequent analysis. Machine learning approaches were employed explore data determine interdependencies features, as well significance fundamental influencing interactions. The XGBoost method used conduct feature importance analysis contributing consumption. results revealed primary behavioral consumption, confirmed influence human while considering its diverse population.

Language: Английский

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GIS-based spatiotemporal analysis for road traffic crashes; in support of sustainable transportation Planning

Transportation Research Interdisciplinary Perspectives, Journal Year: 2023, Volume and Issue: 20, P. 100836 - 100836

Published: May 18, 2023

Road traffic crashes pose a significant challenge worldwide, necessitating increased efforts to reduce them and promote sustainable transport systems. This study aimed investigate spatiotemporal road their causes in the State of Qatar by identifying hot spots crashs exploring whether they were primiarly attributed behavioural practices and/or geometrical design roads intersections. The employed various methods, including Time-Space Cube analysis, Geographically Weighted Regression (GWR), Emerging Hot Spot Spatial Autocorrelation with historical crash data from 2015 2019. findings indicated that mainly concentrated central-eastern region are related driver behaviour. analysis also revealed during weekdays 2019 more strongly clustered than 2015, suggesting probable systematic cause crashes. results provide valuable information for policymakers target high-incidence locations, prioritize interventions develop effective measures policies transportation system Qatar. Overall, this highlights importance continued research policy development area could potentially be applicable transferable similar regions.

Language: Английский

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Modeling and forecasting electricity consumption amid the COVID-19 pandemic: Machine learning vs. nonlinear econometric time series models

Sustainable Cities and Society, Journal Year: 2023, Volume and Issue: 98, P. 104860 - 104860

Published: Aug. 15, 2023

Accurately modelling and forecasting electricity consumption remains a challenging task due to the large number of statistical properties that characterize this time series such as seasonality, trend, sudden changes, slow decay autocrrelation function, among many others. This study contributes literature by using comparing four advanced econometrics models, machine learning deep models1 analyze forecast during COVID-19 pre-lockdown, lockdown, releasing-lockdown, post-lockdown phases. Monthly data on Qatar's total has been used from January 2010 December 2021. The empirical findings demonstrate both econometric models are able capture most important features characterizing consumption. In particular, it is found climate change based factors, e.g temperature, rainfall, mean sea-level pressure wind speed, key determinants terms forecasting, results indicate autoregressive fractionally integrated moving average three state markov switching with exogenous variables outperform all other models. Policy implications energy-environmental recommendations proposed discussed.

Language: Английский

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Analysis of mode choice affects from the introduction of Doha Metro using machine learning and statistical analysis

Transportation Research Interdisciplinary Perspectives, Journal Year: 2023, Volume and Issue: 20, P. 100852 - 100852

Published: May 31, 2023

The aim of this study was to investigate the possible influences operation new Doha Metro on travel mode choice behavior in City, Qatar. Revealed preference (RP) and stated (SP) survey questionnaires were designed collect necessary data. questions considered different trip conditions socioeconomic factors travelers. Three choices study: private cars, taxi services, metro. Two statistical models one machine learning model used analyze current future choices: discrete binary logit (BL) multinomial (MNL) as well extreme gradient boosting (XGBoost). Furthermore, SHapley Additive exPlanations (SHAP) method rank input features based their importance according mean SHAP value. results showed that XGBoost outperforms other two terms predicting its accuracy. various characteristics are significant determining choice, including number travelers bags, journey time, reimbursement parking fees. proved be for choices, nationality, income, age, employment status, vehicle ownership.

Language: Английский

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Modes of summertime thermal urban stress over major cities in the Middle East: A comprehensive assessment of heat exposure risks

Sustainable Cities and Society, Journal Year: 2024, Volume and Issue: 102, P. 105236 - 105236

Published: Jan. 25, 2024

Language: Английский

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Geospatial assessment of the carbon footprint of water and electricity consumption in residential buildings in Doha, Qatar

Journal of Cleaner Production, Journal Year: 2024, Volume and Issue: 445, P. 141262 - 141262

Published: Feb. 16, 2024

The process of estimating the carbon footprint (CF) has become a key method for managing greenhouse gas (GHG) emissions, guiding strategies emission reduction and validating those strategies. Given complexity quantifying total lifecycle emissions in residential buildings, this study delves into assessing CF focusing on water electricity consumption two types buildings: mainly villas flats. This assessment was carried out Doha City, Qatar, using data from 2017 to 2020. employs Multi-Regional Input-Output Life Cycle Assessment (MRIO-LCA) model calculate convert these buildings. Further, various methods statistical spatial analysis including geographically weighted regression (GWR), Ordinary Least Squares (OLS), hotspot cold spot assessments. annual buildings are approximately 7 MtCO2 equivalent, with contributing about 83% total. Concurrently, average is around 0.06 predominantly attributed villas. findings highlight substantial impact structures, particularly villas, city's overall emissions. Furthermore, underscore significant especially Doha's revealing marked seasonal increase, during summer months notable spike reveals consistent clustering patterns across different seasons Elevated concentrated central, northern, northeastern regions, while spots eastern southern areas. Understanding settings crucial developing reduce enhance energy efficiency, address climate change. research helps inform targeted interventions more sustainable use, aligning broader environmental goals.

Language: Английский

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Geospatial modelling of seasonal water and electricity consumption in Doha's residential buildings using multiscale geographically weighted regression (MGWR) and Bootstrap analysis

Sustainable Cities and Society, Journal Year: 2024, Volume and Issue: 113, P. 105654 - 105654

Published: July 9, 2024

Ensuring sustainable water and electricity consumption in urban residential buildings is a growing challenge worldwide, particularly rapidly developing regions with harsh climates. This study examines the seasonal variation of Doha, Qatar, exploring interconnectedness land use/land cover (LULC) socio-demographic characteristics household consumption. For this purpose, we employed statistical analysis (i.e. Pearson correlation Bootstrap analysis) advanced geostatistical models, including Geographically Weighted Regression (GWR) Multiscale (MGWR), to analyze monitor spatial variations The methods involved assessing relationship between surface temperature (LST), water-electricity consumption, analyzing impact demographic variables. Key findings indicate significant spatiotemporal influenced by changes LULC such as size structure. highlight need for integrated planning energy policies that consider impacts enhance efficiency sustainability settings. Furthermore, results underscore importance addressing complex interplay development resource policy-making.

Language: Английский

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Time series analysis of environmental quality in the state of Qatar

Energy Policy, Journal Year: 2022, Volume and Issue: 168, P. 113089 - 113089

Published: June 14, 2022

This study investigated the impact of economic growth, electricity consumption, energy and crop production index on environmental quality in Qatar by considering four different types GHGs emissions (carbon dioxide, methane, nitrous oxide, F-GHGs) using a time-series dataset for period 1990–2019. long- short-term impacts between these variables ARDL bounds testing, while stationarity properties were tested applying Zivot–Andrews test. The results indicate that have positive significant relationship with GHGs, growth has negative long term gases. VECM Cranger Toda-Yamamoto causality tests used to understand causal variables, suggest variables. Several key policy implications derived from findings this research sustain state are discussed paper.

Language: Английский

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Assessment of the Impact of Anthropogenic Evolution and Natural Processes on Shoreline Dynamics Using Multi-Temporal Satellite Images and Statistical Analysis

Water, Journal Year: 2023, Volume and Issue: 15(8), P. 1440 - 1440

Published: April 7, 2023

This research aims to examine changes in the eastern part of Qatar’s shoreline from 1982 2018 by means satellite imagery. Five different time periods, namely 1982, 1992, 2002, 2013, and 2018, were analysed determine movements variations. Techniques such as maximum likelihood classification, normalised difference vegetation index, tasselled cap transformation utilised extract data. Linear regression rate statistics used quantify The results indicate that majority accretion is a result human activities coastal construction, land reclamation, building artificial islands, which are associated with high economic activity over past two decades. Significant observed Lusail City, Pearl, Hamad International Airport (HIA). Natural sediment accumulation was also Al Wakra on southern side HIA. In general, there more gains than losses throughout study period, increased twice its previous length. field survey confirmed presence sandy rocky beaches, well protective structures natural limestone rocks concrete reinforcement.

Language: Английский

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Assessing the spatial distribution and accessibility of public and private schools in Qatar: A GIS-based analysis

GEOMATICA, Journal Year: 2024, Volume and Issue: 76(2), P. 100015 - 100015

Published: July 31, 2024

Educational services are essential to the development and well-being of any city, acting as a cornerstone for individual community advancement. The aim this study is analyze spatial distribution accessibility public private schools in Qatar, using various GIS tools inform educational planning policy. methods employed include Kernel Density Analysis visualize concentration schools, Nearest Neighbor assess patterns, Ripley's K-function evaluate clustering across different scales, Location Quotient determine relative school concentrations, Buffer examine proximity land uses hazards. Additionally, Accessibility was conducted calculate travel times distances schools. results reveal significant both urban centers, particularly Doha, with notable disparities between rural areas. Policy implications highlight need strategic placement new improvement existing facilities, targeted interventions underserved regions ensure equitable access quality education all students Qatar.

Language: Английский

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