How residential energy consumption has changed due to COVID-19 pandemic? An agent-based model

Khalil, Mohamad Ali; Fatmi, Mahmudur Rahman

doi:10.1016/j.scs.2022.103832

Cited by 37 publications

(15 citation statements)

References 41 publications

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“…• "Local heating transition-policymakers": Study (Busch et al 2017) Khalil and Fatmi (2022) uses household agents to represent the in-home and out-of-home activities during COVID 19 to analyze the energy demand for electricity and heat. In study (Shen et al 2021), the Linear Upper Confidence Bound (LinUCB) approach trains a single agent for branch selection to detect the leaky branch of the heating grid using home data.…”

Section: Results 1: Agent-based Modeling With An Urban Energy Systemmentioning

confidence: 99%

“…The microgrid consists of a railroad station, PV plants, an urban wind turbine, and a nearby residential area Impact on the local energy grid and business models Shiera et al ( 2019 ) Analysis of social, technical, environmental, and economic factors for a neighborhood of 18,720 households (1290 buildings) Impact on the local energy grid Bellekom et al ( 2016 ) Analysis of the local grid management when increasing the share of prosumers in a local energy grid in the Netherlands Impact on the local energy grid Fichera et al ( 2020b ) Construction of a theoretical model of a local microgrid in southern Italy. Shown are 370 buildings with PV systems impact on the local energy grid and economic efficiency Category: microgrid electricity and heat Haque et al ( 2017 ) Analysis of control mechanism for congestion management in low voltage grid with 100% PV systems and heat pumps in the Netherlands Development of local control strategies for sector coupling heat Shen et al ( 2021 ) Development of the Linear Upper Confidence Bound with the Contextual Bandit method to identify leakage problems in the heat network Method development for leakage problems Kremers ( 2020 ) Develop autonomous decision-making for local energy trading with prosumers Impact on the local energy grid and economic efficiency Hall and Geissler ( 2020 ) Analysis of three building cluster types using building flexibility by the PV system, battery, and heat pump for grid relief at the substation Development of local control strategies for sector coupling heat Loose et al ( 2020 ) Optimization of an energy interconnection network of wastewater heat pump and cogeneration plants in the city of Lemgo, Germany Development of local control strategies for sector coupling heat Khalil and Fatmi ( 2022 ) Analyze energy demands resulting from COVID-19. Energy demands consider domestic and non-domestic activities o...…”

Section: Methodsmentioning

confidence: 99%

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Agent-based modeling (ABM) for urban neighborhood energy systems: literature review and proposal for an all integrative ABM approach

2022

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Advancing the energy transition in real-world urban settings is attracting interest within interdisciplinary research communities. New challenges for local energy balancing arise particularly in urban neighborhoods where densely populated buildings are facing the needs of the heat transition, an increasing use of battery-electric vehicles and an expansion of renewable energies. Agent-based modeling (ABM) is a suitable approach for addressing various interlinked aspects like market mechanisms and processes, technology adoption, different stakeholder roles and the formulation of policy measures. In this work, we analyze peer-reviewed, open-access literature on ABM for energy neighborhoods and discuss key modeling aspects like model purpose and outcome, the logic of agents and decision-making, the treatment of space and time, and empirical grounding. These ABM allow the study of local market mechanisms, local renewable energy generation, microgrids, the unfolding of heat transition, neighborhood mobility and the evaluation of policies like regulation and financial incentives. We find a lack of integrated neighborhood energy assessments that simultaneously look at the different energy forms and applications: heating, electricity, and mobility. We present a consolidated ABM concept that integrates these sectors. Thus, our work contributes to the advancement of ABM and to the understanding of how to promote the transition to a decarbonized society in urban settings.

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Section: Results 1: Agent-based Modeling With An Urban Energy Systemmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

Agent-based modeling (ABM) for urban neighborhood energy systems: literature review and proposal for an all integrative ABM approach

2022

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“…In the world, the ongoing infectious disease has generated health-related demands and energy consumption (Geng et al, 2022 ; Paulson et al, 2021 ; Yu et al, 2022 ). The lockdown policies worldwide had a significant impact on global energy consumption during the epidemic, accompanied by a rise in residential energy consumption (Khalil & Fatmi, 2022 ). The more interesting research direction need to consider the impact on building energy demand during the COVID-19 epidemic.…”

Section: Discussionmentioning

confidence: 99%

Influence of built environment on building energy consumption: a case study in Nanjing, China

Wang

Yang

et al. 2023

Environ Dev Sustain

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Built environment plays a significant role in optimizing building energy consumption. However, few studies have explored the comprehensive effect between built environment metrics on building energy consumption. Thus, this study aims to explore interrelationships between built environment on building energy consumption focused on moderating effect. In this study, we established a built environment measure system from the perspective of land use and land cover, landscape structure and building configuration. This study explored the correlation between built environment and building energy consumption and analyzed the moderating effect of building configuration emphatically. Results show that: for integrated grids group, normalized difference vegetation index (NDVI) and modified normalized difference water index (MNDWI) have a positive influence and impervious area (IA) has a negative influence, with NDVI has the greatest impact. Building floor (BF), building coverage ratio (BCR) and aspect ratio can weaken the positive relationship between NDVI and energy use intensity of grid ( ). BCR weakens the positive effect of MNDWI on . The moderating effect of building configuration on EUI varies in the same grid group and among different grid groups. For sample 1, BCR inhibits the negative effect of mean perimeter–area ration (PARA-MN) on . For sample 2, BF promotes the negative effect of number of patches and land use richness index (R) on . And sky view factor inhibits the positive effect of IA on . This study reveals the pathways of built environment on building energy consumption. As a result, the keys of optimizing building energy consumption are the reasonable planning and optimization of the urban built environment of different land cover. Graphical abstract Supplementary Information The online version contains supplementary material available at 10.1007/s10668-023-02930-w.

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“…Nevertheless, the decrease in electricity usage was lower in countries with less restrictive policies. By focusing on Canadian data, Khalil and Fatmi [18] reported that the regular average in-home duration of each occupation increased by about 80% throughout the disease outbreak, causing a 29% boost in energy utilization. Similarly, Surahman et al [19] confirmed that throughout the coronavirus period, the average annual energy utilization in Indonesia's major cities is higher than prior to the pandemic.…”

Section: Introductionmentioning

confidence: 99%

“…First, to the best of our knowledge, the study provides the first empirical evidence for the case of Romania. Prior studies were focused on a diverse worldwide sample [4,26], several European countries [17], China [9,13,21,[27][28][29][30][31][32], G7 nations [33], Indonesia [19], South Korea [20], United States [32,[34][35][36], Germany and United States [10], Canada [18], United Kingdom [37], United States and China [38], United States and Japan [39], China and Nigeria [40], Turkey [41], various countries with the leading energy sectors by market capitalization [5], emerging economies [2], or advanced and developing nations [42]. Hence, the evidence for emerging market economies is limited.…”

Section: Introductionmentioning

confidence: 99%

How Energy Sector Reacted to COVID‐19 Pandemic? Empirical Evidence from an Emerging Market Economy

Armeanu

Joldeș

Gherghina

2022

Discrete Dynamics in Nature and Society

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The European Union is facing the highest natural gas prices in 15 years, owing largely to an upward trend in electricity prices, which is also on an uphill curve. However, the rise in electricity and natural gas prices is a widespread phenomenon that is being felt not only in Europe but also globally, as economic activity resumes and energy consumption returns to prepandemic levels. Consequently, this paper investigates how COVID-19 influenced the Romanian energy market. To accomplish our goal, we used daily data for variables and market indices that characterize COVID-19 and the energy market from July 1 to December 21, 2021. The results of the GARCH (1, 1) model estimation show that the major performer in Romania’s energy allocation and supply market had the highest conditional variance. In addition, the ARDL model was chosen because of the variable integration mix (order 0 and 1), as well as the VAR and the Granger causality framework. The empirical results of ARDL models provide the first conclusion of the analysis, indicating that the number of short-term connections was greater than long-term connections, which is also explained by the presence of short episodes of high volatility recorded in the investigated time interval. Another conclusion drawn from this study is that COVID-19 cases registered in Europe and around the world have made a significant contribution to explaining the evolution of the energy market, owing to the large number of cases registered in these regions and the level of contagion transmitted from these markets to the energy market. Furthermore, based on the Granger causality test results, only one-way causal relationships were identified from the variables that capture the evolution of the COVID-9 pandemic to the yields of Romanian energy companies. The novelty of this article is the examination of the impact of COVID-19 on the energy market throughout the fourth wave of coronavirus using the GARCH framework, the ARDL model, which allows for the capture of both short- and long-term reactions, the variance decomposition, and the Granger causality test. Because of the ongoing changes in the pandemic’s evolution, additional research on this topic is undoubtedly on the horizon in the near future.

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How residential energy consumption has changed due to COVID-19 pandemic? An agent-based model

Cited by 37 publications

References 41 publications

Agent-based modeling (ABM) for urban neighborhood energy systems: literature review and proposal for an all integrative ABM approach

Agent-based modeling (ABM) for urban neighborhood energy systems: literature review and proposal for an all integrative ABM approach

Influence of built environment on building energy consumption: a case study in Nanjing, China

How Energy Sector Reacted to COVID‐19 Pandemic? Empirical Evidence from an Emerging Market Economy

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