Modeling the electrical energy consumption profile for residential buildings in Iran

Sepehr, Mohammad Noori; Eghtedaei, Reza; Toolabimoghadam, Ali; Noorollahi, Younes; Mohammadi, Mohammad

doi:10.1016/j.scs.2018.05.041

Cited by 63 publications

(33 citation statements)

References 36 publications

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“…Gaglia et al [22], Vogiatzi et al [23] and Chang et al [24] provided in their works different methods and analyses of energy consumption in residential buildings, aimed at identifying energy saving interventions and at reducing pollutants emissions. The authors of [25][26][27][28] were focused only on residential electricity consumptions, so as to provide a model for loads profiling, while the optimal load scheduling, when RES are installed, was the main target for the research projects reported in [29][30][31].…”

Section: Introductionmentioning

confidence: 99%

Energy Use in Residential Buildings: Characterisation for Identifying Flexible Loads by Means of a Questionnaire Survey

2019

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This work shows the outcomes of a research activity aimed at the energy characterization of residential users. Specifically, by data analysis related to the real energy consumption of sample buildings, the flexible loads amount has been identified so as to investigate on the opportunity to implement a demand/response (DR) program. The most meaningful input data have been collected by an on-line questionnaire created within an Excel spreadsheet allowing one to simulate and compare the calculations with the actual dwellings’ consumption; 412 questionnaires have been used as statistical sample and simulations have been performed based on single-zone dynamic model. Additionally, once the energy consumptions have been sorted by the different services, reference key performance indicators (KPIs) have been also calculated normalising those ones by people and house floor surface. From data analysis, it emerges how the Italian residential users are not very electrified. Furthermore, the flexible loads are low and, implementing minor maintenance interventions, the potential of flexibility can decrease up to 20%. For that reason, the current research can be further developed by investigating on suitable flexibility extensions as well as on the automation system requirements which is needed managing the flexible loads.

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Section: Introductionmentioning

confidence: 99%

Energy Use in Residential Buildings: Characterisation for Identifying Flexible Loads by Means of a Questionnaire Survey

2019

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“…The fact of the matter is that the growing consumption of energy sources is believed to be one of the considerable causes of environmental changes [2]. Referring to the United Nations Environment Program in 2016, households consume 40% of the primary energy (i.e., based on natural resources) in the world; hence, they are responsible for one-third of related global greenhouse gas (GHG) emissions [3,4]. In addition, energy as a determinant factor for the growing Iranian economy is mainly derived from natural resources [5].…”

Section: Introductionmentioning

confidence: 99%

Determinants of Variation in Household Energy Choice and Consumption: Case from Mahabad City, Iran

et al. 2019

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This study seeks to find a method to identify the dominant pattern of energy choice and consumption in households, centering on demographic factors affecting the use of home appliances. To this aim, this study dealt with a variety of energy sources widely used by households, namely, liquefied petroleum gas (LPG), electricity, and kerosene for cooking, heating and cooling, lighting, and home appliances. Additionally, significant associations for the household energy choice and consumption were identified for demographic variables including household size, gender, head-of-household age, educational level, and income group. A logistic regression analysis was performed to obtain quantitative data provided by a survey from 821 households across residential districts of urban and rural areas in Mahabad city, northwest Iran. Obtained data were analyzed within a proposed three-energy dimension model (3-ED). The results showed that, in the case of other variables remaining constant, income may lead to variation in LPG and electricity consumption. Unlike other independent variables, the head-of-household age failed to have a significant impact. The findings can contribute to a better understanding of effective factors got household energy choice and consumption in other cities, and they can be useful for the support of policy-makers in their consumption patterns.

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“…The first group is mainly based on statistical approaches to model REC, analyse interactions, forecast REC, and provide recommendations to policy-makers and researchers. The most significant works in this category are (Al-Ghandoor et al, 2009;Biswas et al, 2016;Catalina et al, 2013;Chen et al, 2016;Fracastoro & Serraino, 2011;Fumo & Rafe Biswas, 2015;Hsu, 2015;Iraganaboina & Eluru, 2021;Kavousian et al, 2013;Kim et al, 2020;Rhodes et al, 2014;Sepehr et al, 2018;Štreimikienė, 2014;Theodoridou et al, 2011;Tso & Guan, 2014;Walter & Sohn, 2016;Williams & Gomez, 2016).…”

Section: Literature Reviewmentioning

confidence: 99%

“…These variables can be grouped along five main dimensions influencing REC: socio-demographic, economic, climate, building and urban dimensions. (Hsu, 2015;Kavousian et al, 2013;Kim et al, 2020;Theodoridou et al, 2011) Floor area (Fracastoro & Serraino, 2011;Kavousian et al, 2013;Kim et al, 2020;Theodoridou et al, 2011) Building envelop (Catalina et al, 2013;Theodoridou et al, 2011;Williams & Gomez, 2016) Housing type/size (Hsu, 2015;Tso & Guan, 2014) Energy system and appliance use (Kim et al, 2020;Sepehr et al, 2018;Štreimikienė, 2014;Tso & Guan, 2014) Urbanization rate Location characteristics Settlement pattern Density and mixed use (Chen et al, 2016;Dujardin et al, 2014;Iraganaboina & Eluru, 2021) The second group of literature is mainly based on GIS approaches that create and analyse REC's spatial dimension, considering a number of variables as for instance urban density or climate zone. In this context, several studies based on spatial approaches associated with energy modelling have progressively developed in recent years, in order to help energy planning such as (Caputo et al, 2013;Caputo & Pasetti, 2017;de Santoli et al, 2019;Dujardin et al, 2014;Evola et al, 2016;Fichera et al, 2016;Groppi et al, 2018;Howard et al, 2012;Mattinen et al, 2014;Österbring et al, 2016).…”

Section: Literature Reviewmentioning

confidence: 99%

An energy consumption model for the Algerian residential building's stock, based on a triangular approach: Geographic Information System (GIS), regression analysis and hierarchical cluster analysis

Afaifia

Djiar

Bich-Ngoc

et al. 2021

Sustainable Cities and Society

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Modeling the electrical energy consumption profile for residential buildings in Iran

Cited by 63 publications

References 36 publications

Energy Use in Residential Buildings: Characterisation for Identifying Flexible Loads by Means of a Questionnaire Survey

Energy Use in Residential Buildings: Characterisation for Identifying Flexible Loads by Means of a Questionnaire Survey

Determinants of Variation in Household Energy Choice and Consumption: Case from Mahabad City, Iran

An energy consumption model for the Algerian residential building's stock, based on a triangular approach: Geographic Information System (GIS), regression analysis and hierarchical cluster analysis

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