Building energy retrofit index for policy making and decision support at regional and national scales

Khayatian, Fazel; Sarto, Luca; Dall’O’, Giuliano

doi:10.1016/j.apenergy.2017.08.237

Cited by 33 publications

(14 citation statements)

References 63 publications

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“…The removal of tax incentives and subsidies affects the sustainable development of existing buildings [60]. On the contrary, the government's financial or policy incentive guarantee affects stakeholders' action to implement green retrofitting projects in existing buildings [61]. At present, most studies are based on the perspective of direct government intervention.…”

Section: Relationship Between Policy Factors and Green Retrofitting Imentioning

confidence: 99%

“…If the government can provide appropriate economic subsidies according to the green retrofitting area, then I am willing to carry out the green retrofitting of existing residential buildings. [54,59,61] If the government issues tax incentives for green retrofitting, then I am willing to carry out the green retrofitting of existing residential buildings. If the government introduces a preferential policy on loan interest rates for green retrofitting, then I am willing to carry out the green retrofitting of existing residential buildings.…”

Section: Pfmentioning

confidence: 99%

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Factors Influencing Residents’ Intention toward Green Retrofitting of Existing Residential Buildings

Zhao

Shen

et al. 2019

Sustainability

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The green retrofitting of existing residential buildings is an important approach to realise the sustainable development of stock buildings. In addition to the new technologies and materials related to green retrofitting, the intention of residents toward the green retrofitting of existing residential buildings must be understood. However, the factors affecting such intentions are still unclear. Hence, this study refers to the extended theory of planed behaviour and constructs a theoretical model of the intention toward the green retrofitting of existing residential buildings. On the basis of the data from 507 questionnaires collected from eastern and western China, the theoretical model is tested via structural equation modelling (SEM). Multigroup SEM is used to analyse the differences in population characteristics and the intention of residents toward the green retrofitting of existing residential buildings in residential areas. Research results reveal the following: (1) the most important factors affecting residents’ intention toward green retrofitting are policy factors, followed by cognition of green retrofitting, behaviour, subjective norms, and perceived behavioural control; (2) policy factors not only directly affect residents’ intention toward green retrofitting but also indirectly affect their intention toward existing residential green retrofitting through perceived behavioural control; (3) residents’ cognition of green retrofitting exerts no significant direct impact on their intention toward green retrofitting, but it does indirectly affect their intention toward green retrofitting through behaviour and subjective norms; (4) behaviour, subjective norms, and perceived behavioural control have direct and significant influences on intention toward green retrofitting; (5) demographic characteristics (gender, age, monthly family income, education level, and occupation) and regional variables (east and west) present significant differences in different influence paths. The conclusion of the study provides a targeted path reference for the promotion of the green retrofitting of existing residential buildings.

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Section: Relationship Between Policy Factors and Green Retrofitting Imentioning

confidence: 99%

Section: Pfmentioning

confidence: 99%

Factors Influencing Residents’ Intention toward Green Retrofitting of Existing Residential Buildings

Zhao

Shen

et al. 2019

Sustainability

View full text Add to dashboard Cite

show abstract

“…In general, the equation for EEI is given as the ratio of the energy input to the factor related to the energy using component [20]. Many researchers study EEI as a building index that incorporates tracking of performance of energy consumption in buildings which controls the usage of energy without exceeding the proposed baseline [19,[21][22][23][24]. S. Moghimi et al [19] reported that most organizations implement EEI analysis for buildings using kWh/m², where energy consumption is based on the parameter of per unit floor area.…”

Section: Energy Eficiency Indexmentioning

confidence: 99%

Energy efficiency index by considering number of occupants: a study on the lecture rooms in a university building

Zakaria

Hassan

Abdullah

et al. 2019

IJEECS

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<span>The building sector is attributed to approximately 40% of the nation’s energy consumption and this accounts for a significant percentage of the nation’s energy consumption. For this reason, energy efficiency in buildings has now become an important subject in the national energy scenario. Energy Efficiency Index (EEI) is one of the energy consumption indicators that is widely used in the building sector for measuring energy performance. This index is generally measured based on the energy used per unit of building floor area. However, this index is not able to directly identify other factors affecting energy usage. This paper suggests an Energy Efficiency Index (EEI) for determining the performance of lecturer rooms in a university building. Unlike the conventional EEI, the proposed EEI determines the room’s energy usage performance by considering the number of occupants. The study was conducted at the Faculty of Electrical Engineering, Universiti Teknologi Malaysia (UTM) and the results show that the number of occupants significantly influences the energy usage performance of rooms in a university building.</span>

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“…The aim of this work is to identify general classes of buildings according to their electric energy consumption and to improve not only the demand estimations, but also the aggregation of electric energy profiles of different buildings, which results truly useful for centralized energy purchasing, energy consumption monitoring by activity sectors and fast identification of abnormal consumption behaviors. Other applications of the proposed method are related to the optimization of the definition of Power Purchase Agreements (PPAs) in the public sector or the optimal introduction of the Electric Vehicle (EV), among many others [4][5][6][7]. Taking into account a proposed case study, this methodology has been applied to the health system stock of the Castilla y León region in Spain, which consists of approximately 250 buildings of different sizes and final uses.…”

Section: Introductionmentioning

confidence: 99%

Electrical Consumption Profile Clusterization: Spanish Castilla y León Regional Health Services Building Stock as a Case Study

et al. 2018

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Health Services building stock is usually the top energy consumer in the Administrative sector, by a considerable margin. Therefore, energy consumption supervision, prediction, and improvement should be carried out for this group in a preferential manner. Most prior studies in this field have characterized the energy consumption of buildings based on complex simulations, which tend to be limited by modelisation restrictions and assumptions. In this paper, an improved method for the clusterization of buildings based on their electrical energy consumption is proposed and, then, reference profiles are determined by examining the variation of energy consumption over the typical yearly consumption period. The temporary variation has been analyzed by evaluating the temporary evolution of the area consumption index through data mining and statistical clusterization techniques. The proposed methodology has been applied to building stock of the Health Services in the Castilla y León region in Spain, based on three years of historical monthly electrical energy consumption data for over 250 buildings. This building stock consists of hospitals, health centers (with and without emergency services) and a miscellaneous set of administrative and residential buildings. Results reveal five distinct electrical consumption profiles that have been associated with five reference buildings, permitting significant improvement in the demand estimation as compared to merely using the classical energy consumption indicators.

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Building energy retrofit index for policy making and decision support at regional and national scales

Cited by 33 publications

References 63 publications

Factors Influencing Residents’ Intention toward Green Retrofitting of Existing Residential Buildings

Factors Influencing Residents’ Intention toward Green Retrofitting of Existing Residential Buildings

Energy efficiency index by considering number of occupants: a study on the lecture rooms in a university building

Electrical Consumption Profile Clusterization: Spanish Castilla y León Regional Health Services Building Stock as a Case Study

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