Big meter data analysis of the energy efficiency potential in Stockholm's building stock

Shahrokni, Hossein; Levihn, Fabian; Brandt, Nils

doi:10.1016/j.enbuild.2014.04.017

Cited by 58 publications

(37 citation statements)

References 17 publications

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“…As the amount of data continues to increase in new studies, such as in Refs. [32], variable selection methods are necessary to identify key determinants of building energy consumption.…”

Section: "Big" Data Approachesmentioning

confidence: 99%

Identifying key variables and interactions in statistical models of building energy consumption using regularization

Hsu

2015

Energy

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a b s t r a c tStatistical models can only be as good as the data put into them. Data about energy consumption continues to grow, particularly its non-technical aspects, but these variables are often interpreted differently among disciplines, datasets, and contexts. Selecting key variables and interactions is therefore an important step in achieving more accurate predictions, better interpretation, and identification of key subgroups for further analysis.This paper therefore makes two main contributions to the modeling and analysis of energy consumption of buildings. First, it introduces regularization, also known as penalized regression, for principled selection of variables and interactions. Second, this approach is demonstrated by application to a comprehensive dataset of energy consumption for commercial office and multifamily buildings in New York City. Using cross-validation, this paper finds that a newly-developed method, hierarchical grouplasso regularization, significantly outperforms ridge, lasso, elastic net and ordinary least squares approaches in terms of prediction accuracy; develops a parsimonious model for large New York City buildings; and identifies several interactions between technical and non-technical parameters for further analysis, policy development and targeting. This method is generalizable to other local contexts, and is likely to be useful for the modeling of other sectors of energy consumption as well.

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“…As the amount of data continues to increase in new studies, such as in Refs. [32], variable selection methods are necessary to identify key determinants of building energy consumption.…”

Section: "Big" Data Approachesmentioning

confidence: 99%

Identifying key variables and interactions in statistical models of building energy consumption using regularization

Hsu

2015

Energy

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“…Heat consumption of a typical project in heating season following heat meterinḡ t in, 1 Average indoor temperature of a typical project in heating season prior to heat meterinḡ t in, 2 Average indoor temperature of a typical project in a heating season following heat meterinḡ tout,1…”

Section: Appendix Amentioning

confidence: 99%

“…There have been tremendous researches and explorations conducted in many countries. Among all measures of slashing dependence on fossil fuels, heat metering has been considered important for energy efficiency; therefore actual energy saving of metering has drawn increasing attention worldwide [1][2][3]. According to EU directive 2012/27/EU, independent heat metering devices must be installed to measure heat consumption and domestic hot water consumption in multiapartment buildings by December 31, 2016 [4].…”

Section: Unbalanced Heat Metering Status In Countries and Regionsmentioning

confidence: 99%

Establishment and validation of a sustainable evaluation model for heat metering technology in China

Yuan

2015

Energy and Buildings

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“…Some studies have concerned the optimization of thermal insulation (Ucar and Balo 2009;Yu et al 2009;Daouas 2011;Cuce et al 2014), others the improvement of the plant efficiency (Bizzarri and Morini 2006;Zhao et al 2008Zhao et al , 2009Chan et al 2010;Terlizzese and Zanchini 2011;Brignoli et al 2013;Liu et al 2013;Naldi et al 2014), others the energy-saving potential (Balaras et al 2000;Amstalden et al 2007;Ascione et al 2011;Chidiac et al 2011a, b;Dall'O' and Sarto 2013;Shahrokni et al 2014), others again the effects of solar chimneys, permeable coverings, cool roofs and green roofs (Afonso and Oliveira 2000; Orosa and Oliveira Ucar and Balo (Ucar and Balo 2009) determined the optimum insulation thickness of external walls for four cities with different climatic conditions in Turkey, with reference to Foamboard, extruded polystyrene and fiberglass as insulation materials. Yu et al (Yu et al 2009) studied the optimization of the insulation thicknesses of expanded polystyrene, extruded polystyrene, foamed polyurethane, perlite, and foamed polyvinyl chloride, for a typical residential wall in China.…”

Section: Introductionmentioning

confidence: 99%

Planned energy-efficient retrofitting of a residential building in Italy

Zanchini¹,

Naldi²,

Lazzari

et al. 2015

Future Cities and Environment

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The planned energy-efficient retrofitting of a residential building in Bologna, North-Center Italy is presented. The building is a detached house with an unheated basement, three floors with 2 apartments each, and an unheated attic. The total heated floor area is 281.9 m2. The external wall is made of solid brick masonry and most Windows are single glazed; no thermal insulation is present. Space heating is supplied by a gas boiler and radiators in the rooms. DHW is supplied by single-apartment electric boilers in 5 apartments and by a gas boiler in one apartment.\ud Lighting is obtained by incandescent lamps. The proposed retrofitting includes: external thermal insulation of the vertical walls by calcium silicate hydrates and loft insulation by mineral wool; replacement of windows; installation of a multifunction air-to-water heat pump for heating, cooling and DHW; replacement of the radiators by new heat exchangers; LED lighting; installation of PV\ud panels. The building has been simulated by TRNSYS 17, and the heat pump has been simulated by own MATLAB codes. The retrofitting will reduce the total annual use of primary energy (excluding appliances) from 332.5 to 44.8 kWh/m2, and will yield an important improvement of thermal comfort

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Big meter data analysis of the energy efficiency potential in Stockholm's building stock

Cited by 58 publications

References 17 publications

Identifying key variables and interactions in statistical models of building energy consumption using regularization

Identifying key variables and interactions in statistical models of building energy consumption using regularization

Establishment and validation of a sustainable evaluation model for heat metering technology in China

Planned energy-efficient retrofitting of a residential building in Italy

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