Towards a computer program for building-integrated wind technologies

elmokadem, ashraf; Megahed, Naglaa A.; Noaman, Dina S.

doi:10.1016/j.enbuild.2016.02.022

Cited by 7 publications

(3 citation statements)

References 5 publications

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“…Wind turbines are expected to be installed in close proximity to the city in which the wind speed is adjusted to match the urban context according to Ref. [61]. The Durisch model [62], which considers the global solar irradiation on the tilted SPV panel, cell temperature, and air-mass is used to compute the efficiency of the SPV panels.…”

Section: 1) Energy and Cash Flow Modelmentioning

confidence: 99%

Quantifying the impact of urban climate by extending the boundaries of urban energy system modeling

et al. 2018

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Rapid growth of cities, concerns on global warming and depletion of fossil fuel resources call for sustainable energy solutions for cities. Distributed energy systems such as energy hubs offer promising solutions in this context. Evaluating the energy demand at urban scale is vital to support the design of energy hubs. However, most of the recent studies are based on bottom-up models and do not consider the energy demand in detail. More specifically, the influence of the urban climate on urban energy demand has not been considered so far in the energy system design process. In order to address this research gap, a novel computational platform is developed in the first part of this study, combining an urban climate model with a building simulation tool and an energy system optimization model. The second part of the manuscript is devoted to quantifying the impact of urban climate on energy system design and assessing the consequences of neglecting this specific aspect on energy system performance. Three case studies are conducted considering three building densities for the city of Nablus (building density at the periphery, center and future center of the city) in Palestine. Three scenarios representing 1) standalone buildings (present practice) 2) shadowing and longwave reflection (radiation heat transfer from the walls and the roofs of the buildings to the urban climate and to the sky) of neighboring buildings and 3) urban climate are considered for each case study when computing the energy demand. Subsequently, the energy system is optimized considering Net Present Value (NPV) and system autonomy level as the objective functions (Pareto optimization). The results of the study reveal that the urban climate has a notable impact on the energy demand and energy system design. More importantly, it is shown that the influence of urban climate results in higher fluctuations in the energy demand, which in turn results in a notable increase in the NPV (by up to 40%). This further magnifies the increase in annual or peak demand. The study reveals that neglecting the influence of urban climate in the energy system design process can result in a performance gap in NPV, grid integration level, and greenhouse gas emissions and can impose reliability issues. The design tool introduced in this study can be used for urban planning to mitigate the aforementioned adverse effects.

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Section: 1) Energy and Cash Flow Modelmentioning

confidence: 99%

Quantifying the impact of urban climate by extending the boundaries of urban energy system modeling

et al. 2018

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“…The energy consumption of the building sector is increasing as the economy grows and the demand for interior thermal comfort rises [3][4][5]. Accordingly, architects and engineers should explore strategies to minimize the amount of fossil fuels used in building design for heating and cooling [6]. Many solutions for improving thermal comfort have already been implemented, including shading, courtyards, green roofs, natural ventilation, mass creation, and thermal energy storage (TES) [7][8][9][10][11][12][13][14].…”

Section: Introductionmentioning

confidence: 99%

The Strategy of Using PCMs in Building Sector Applications

Ismail

Megahed

Eltarabily

2022

Port-Said Engineering Research Journal

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With the recent large increase in the building sector's energy use to provide thermal comfort to consumers, the restrictions of climate change, and the scarcity of energy supplies, there has been a need to develop ways to reduce energy consumption. The use of phase change materials (PCMs) as a thermal energy storage (TES) system has attracted a lot of attention as a technique to improve thermal performance, conserve energy, and improve occupant comfort. When storing energy via phase change, the key advantage in terms of building materials is the higher energy density, which indicates that more energy can be stored in a constant volume. This study focuses on the vital role of PCMs in building applications by presenting the different classifications of PCMs as well as their integration techniques, systems, and benefits for building applications. Different case studies for full scale buildings are presented. A design strategy is concluded for integrating PCMs in buildings.

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“…Thus, the urban energy resilience (UER) concept is seeing increasing application in the fields of climate change, sustainability, and natural disaster risk analysis. Meanwhile, climate change and global warming present numerous challenges to energy resources in cities and urban areas (Ismail et al, 2022b;EL-Mokadem et al, 2016b;Noaman et al, 2022;Perera et al, 2021;Sharifi and Yamagata, 2014a). In fact, 60 to 80% of global energy is consumed in urban areas (Hassan et al, 2020a(Hassan et al, , 2020bOlazabal et al, 2012;Sharifi, 2019;Sharifi and Yamagata, 2016a), which can lead to negative consequences, for instance an increase in blackouts and grid failures (Erker et al, 2017;Sharifi and Yamagata, 2016a).…”

Section: Introductionmentioning

confidence: 99%

Improvingurban Energyresiliencewith an Integrative Framework Based on Machine Learningmethods

Hassan

Megahed²

2022

A&J

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Introduction:Climate change and global warming are among the greatest challenges facing the world today. A newconcept, known as urban resilience, has been developed in response. There are various approaches to urban resilience. Among them, is the urban energy resilience (UER) approach, which poses a considerable challenge. Machine learning (ML), as an application of artificial intelligence (AI), provides powerful and affordable computing resources, large-scale datamining, advanced algorithms, and real-timemonitoring. However, veryfewstudies have investigated howsuch aspectscan be integrated into urban resilience in general, and UER in particular. Purpose of the study: The study develops an integrative framework that can improve UER, based on ML methods. Methodology: We carried out a bibliometric analysisand a systematic review of UER in accordance with AI concepts, models, and applications. Results:The findings of this study were used to create an integrative framework, based on three hierarchical phases, which effectively addressed the main capabilities of UER, identified its priorities, and shed light on how ML can benefit UER as a whole. Novelty:The framework developed in this study also offers insights in integrating ML methods into UER as strategically as possible, especially in the context of climate change and urban energy systems. This framework can serve as reference for specialistsand decision-makers aiming to expand AI and ML applications to optimize UER.

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Towards a computer program for building-integrated wind technologies

Cited by 7 publications

References 5 publications

Quantifying the impact of urban climate by extending the boundaries of urban energy system modeling

Quantifying the impact of urban climate by extending the boundaries of urban energy system modeling

The Strategy of Using PCMs in Building Sector Applications

Improvingurban Energyresiliencewith an Integrative Framework Based on Machine Learningmethods

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