Influence of exterior door opening on the heating consumption of a passive residential house

Lukić, Nebojša; Nešović, Aleksandar; Nikolić, Novak

doi:10.1007/s12053-020-09880-6

Cited by 4 publications

(3 citation statements)

References 19 publications

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“…Стога је отишла корак даље, и то промовисањем нових концепата енергетски ефикасних зграда: ниско енергетске зграде 2 (Abel, 1994), пасивне зграде 3 (Altan et al, 2016), зграде нулте енергије 4 (Voss et al, 2011), аутономне зграде 5 (Ramanathan, 2021) и зграде са вишком енергије 6 (Firląg, 2019). Поменути концепти се све више преливају и на Србију, па су постојеће традиционалне зграде, како време одмиче, у све неповољнијој позицији (Bojić et al, 2011;Matić et al, 2016;Lukić et al, 2020;Nešović et al, 2023). Биоклиматско-пасивни приступ у зградарству, базиран на промовисању земуница 7 -зграда прекривених земљиштем, делимично или потпуно, одавно је присутан у светским научним круговима (Akubue, 2021;Roy, 2006).…”

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“…Крстић и остали показују да су земунице архитектонски концепт који поседује велики енергетски потенцијал (Krstić et al, 2018). Кроз неколико научних радова, Милановић је истражио архитектонске и грађевинске аспекте земуница (Milanović, 2016;2017, Milanovic et al, 2018, док су енергетски аспекти и потенцијали земуница и нумерички истражени (Lukić et al, 2019;Nešović et al, 2019;Nešović et al, 2023 Почетни симулациони модел (Сл. 1) формиран је у складу са националним прописима о енергетској ефикасности у зградарству (Službeni glasnik RS, 2011).…”

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Energy performance of infiltrated and elevational earthsheltered buildings in the territory of the city of Kragujevac: A numerical investigation

Nešović

2024

Arhitektura i urbanizam

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This paper investigates the energy potential of different modern earth-sheltered building types, during a seven-month period (October 1 to April 30), in order to achieve sustainable development. The subject of the research is a one-story residential building (total net area 102.5 m2), intended for a family of four, located in the territory of the city of Kragujevac (central Serbia). The heating system consists of a ground source heat pump, geothermal vertical probes, floor panel heaters, and two circulation pumps. Four types of infiltrated earth-sheltered buildings, as well as one elevation earth-sheltered building, were investigated by numerical analysis (using the EnergyPlus software). The energy performances of all of the mentioned earth-sheltered buildings were compared with the energy performance of classic above-ground buildings. All of the buildings have the same geometric, construction, and thermo-technical performance. The research results show that, in infiltrated earth-sheltered buildings, the annual consumption of final (electrical) energy can be reduced by 2.53-21.64% (depending on the number of external building elements in direct contact with the soil). Modern architecture should still be focused on the development of elevation earth-sheltered buildings because in them the annual consumption of final (electrical) energy for heating can be reduced by more than 40%.

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Energy performance of infiltrated and elevational earthsheltered buildings in the territory of the city of Kragujevac: A numerical investigation

Nešović

2024

Arhitektura i urbanizam

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“…But, if the sample size is small, it provides inaccuracy, and its cost is inefficient due to the larger dataset. ERV (Luki c et al, 2020) is used to prevent leakage problems and enhance the lifetime, and it is used to reduce cost. But, it needs more service to maintain regularly, and it requires more energy and usage of electricity.…”

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Analyzing the opening and closing of windows in residential for predicting the energy consumption using optimized multi-scale convolution networks

Sivapriya,

Subbaiyan

2024

IJIUS

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PurposeThis proposal aims to forecast energy consumption in residential buildings based on the effect of opening and closing windows by the deep architecture approach. In this task, the developed model has three stages: (1) collection of data, (2) feature extraction and (3) prediction. Initially, the data for the closing and opening frequency of the window are taken from the manually collected datasets. After that, the weighted feature extraction is performed in the collected data. The attained weighted feature is fed to predict energy consumption. The prediction uses the efficient hybrid multi-scale convolution networks (EHMSCN), where two deep structured architectures like a deep temporal context network and one-dimensional deep convolutional neural network. Here, the parameter optimization takes place with the hybrid algorithm named jumping rate-based grasshopper lemur optimization (JR-GLO). The core aim of this energy consumption model is to predict the consumption of energy accurately based on the effect of opening and closing windows. Therefore, the offered energy consumption prediction approach is analyzed over various measures and attains an accurate performance rate than the conventional techniques.Design/methodology/approachAn EHMSCN-aided energy consumption prediction model is developed to forecast the amount of energy usage during the opening and closing of windows accurately. The emission of CO2 in indoor spaces is highly reduced.FindingsThe MASE measure of the proposed model was 52.55, 43.83, 42.01 and 36.81% higher than ANN, CNN, DTCN and 1DCNN.Originality/valueThe findings of the suggested model in residences were attained high-quality measures with high accuracy, precision and variance.

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Impact of heat source at radiant electric heating panel

Cvetković

Nešović

2021

Energy and Buildings

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Influence of exterior door opening on the heating consumption of a passive residential house

Cited by 4 publications

References 19 publications

Energy performance of infiltrated and elevational earthsheltered buildings in the territory of the city of Kragujevac: A numerical investigation

Energy performance of infiltrated and elevational earthsheltered buildings in the territory of the city of Kragujevac: A numerical investigation

Analyzing the opening and closing of windows in residential for predicting the energy consumption using optimized multi-scale convolution networks

Impact of heat source at radiant electric heating panel

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