Building thermal energy modeling with loss minimization

Bondarenko, Daniel; Hussain, Sajid; Musharavati, Farayi; Pokharel, Shaligram

doi:10.1016/j.simpat.2014.08.006

Cited by 7 publications

(6 citation statements)

References 12 publications

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“…The algorithm of generating feasible scenarios allow effective evaluation and optimization of all energy components of the building. Compared with efforts that was conducted in [4][5][6]11,13,16] to evaluate some selected scenarios, the proposed algorithm offer a wide range of energy technologies that can achieve multiple objectives.…”

Section: Resultsmentioning

confidence: 99%

“…This is because there is a lack of significant tools to produce all possible scenarios for energy evaluation. In [5,6,11] an evaluation of one scenario, according to implicational knowledge of the authors, has been conducted. The authors highlighted the significance of applying thermal insulation on buildings to reduce energy consumption.…”

Section: Problem Formulation and Esn Objectivesmentioning

confidence: 99%

“…For complex buildings with complicated energy demands, the energy con-servation measures, sources and loads design parameters are a challenge to identify. In [11], the author developed building thermal performance ESN that study the effect of insulation materials, dimensions, loads, and schedules of people interactions on selected KPIs. The effort in their work was to evaluate different Heat energy conservation technologies with respect to the dynamical thermal changes, and track the dominant sinks resulting from these changes.…”

Section: Introductionmentioning

confidence: 99%

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Energy Semantic Network for Building Energy Management

Eldessouky

2015

Intell Ind Syst

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This paper proposes an engineering design framework and methodology for building an energy semantic network (ESN) which helps to create and evaluate possible scenarios of energy system structure of buildings (energy generation, conversion, and conservation measures). The developed ESN does not only focus on building thermal performance (i.e. insulation materials, geometry, and dynamic behavior of building occupants) but also integrates energy resources, Hybrid Energy Supply Unit, and source-load interconnection. In other words, the developed ESN supports the design and evaluation of micro energy grid for building with various options of energy generation and conversion. Parameters identification of building energy system and their range is an important step to successfully develop energy optimization within buildings. The developed ESN generates the possible scenarios of energy generation and conversion for evaluation and optimization purposes. The structure of the ESN provides heterogeneous presentation of the classes with flexible architecture to modify, add, or delete significant energy classes. In addition, the ESN structure is designed to avoid non-realistic computational burden during simulations and evaluation. The methodology of generating the minimal possible scenarios is introduced. For validation purposes, the proposed algorithm is examined using case study of a mid-size house with different energy sources and thermal zones. The ESN of this case study is discussed, the possible scenarios of energy generation, conversion and load type are produced and their simulation and B Hossam A.

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Section: Resultsmentioning

confidence: 99%

Section: Problem Formulation and Esn Objectivesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Energy Semantic Network for Building Energy Management

Eldessouky

2015

Intell Ind Syst

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“…The rotating turbine is connected to a generator that generates electricity. This technology has been successfully implemented in various contexts [62][63][64]. The amount of power generated at the household level is relatively small [65][66][67].…”

Section: Pico Hydroelectricitymentioning

confidence: 99%

Perspectives on Dual-Purpose Smart Water Power Infrastructures for Households in Arid Regions

Alghool¹,

Al-Khalfan²,

Attiya³

et al. 2019

Electric Power Conversion

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In hot arid climates, freshwater and power are produced simultaneously through seawater desalination since these regions receive little rainfall. This results in a unique urban water/power cycle that often faces sustainability and resilience challenges. Elsewhere, such challenges have been addressed through smart grid technologies. This chapter explores opportunities and initiatives for implementing smart grid technologies at household level for a case study in Qatar. A functional dual-purpose smart water/power nanogrid is developed. The nanogrid includes multiloop systems for on-site water recycling and on-site power generation based on sustainability concepts. A prototype dual-purpose GSM-based smart water/ power nanogrid is assembled and tested in a laboratory. Results of case study implementation show that the proposed nanogrid can reduce energy and water consumptions at household level by 25 and 20%, respectively. Economic analysis shows that implementing the nanogrid at household level has a payback period of 10 years. Hence, larger-scale projects may improve investment paybacks. Extension of the nanogrid into a resilient communal microgrid and/or mesogrid is discussed based on the concept of energy semantics. The modularity of the nanogrid allows the design to be adapted for different scale applications. Perspectives on how the nanogrid can be expanded for large scale applications are outlined.

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“…Practitioners would use BPS tools for predicting overheating, calculating heating and cooling loads, sizing equipment , evaluating alternative technologies (energy efficiency and renewable energy), regulatory compliance, or more recently, integrated performance design or rating (Crawley 2008). In several design methodologies, BPS serves as an integrated, well-performing support tool for optimising the entire design process (Anderson, Christensen, and Horowitz 2006) (Ferrara et al 2014) (Gabbar et al 2014) (Griego, Krarti, and Hernandez-Guerrero 2015) (Kanagaraj and Mahalingam 2011) (Pacheco, Ordóñez, and Martínez 2012) (Saari et al 2012) (Tibi, Ghaddar, and Ghali 2013). BPS is widely used in different phases in the life cycle of a building project.…”

Section: Overview Of Building Performance Simulationmentioning

confidence: 99%

A library of building occupant behaviour models represented in a standardised schema

Bélafi

Hong

Reith³

2018

Energy Efficiency

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Over the past four decades, a substantial body of literature has explored the impacts of occupant behaviour (OB) on building technologies, operation and energy consumption. A large number of data-driven behavioural models has been developed based on field data. These models lack standardization and consistency, leading to difficulties in applications and comparison. To address this problem, an ontology was developed using the Drivers-Needs-Actions-Systems (DNAS) framework. Recent work has been carried out to implement the theoretical DNAS framework into an eXtensible Markup Language (XML) schema titled 'occupant behaviour XML' (obXML), which is a practical implementation of OB models that can be integrated into building performance simulation (BPS) programs. This paper presents a newly developed library of OB models represented in the standardised obXML schema format. This library provides ready-to-use examples for BPS users to employ more detailed and robust occupant representation in their energy models. The library, which contains an initial repository of 52 OB models, has been made publicly available for building energy professionals and the BPS community. As part of the library development process, the limitations of the obXML schema were identified and addressed, and future improvements were proposed.

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Building thermal energy modeling with loss minimization

Cited by 7 publications

References 12 publications

Energy Semantic Network for Building Energy Management

Energy Semantic Network for Building Energy Management

Perspectives on Dual-Purpose Smart Water Power Infrastructures for Households in Arid Regions

A library of building occupant behaviour models represented in a standardised schema

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