Assessment of energy efficiency for traditional non-engineered and engineered residential buildings – A case of North-Eastern India

Pal, Rituparna; Roy, Sayahnya; Thakur, Biswajit

doi:10.1016/j.matpr.2021.11.538

Cited by 4 publications

(2 citation statements)

References 10 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…To produce thermal comfort, this action uses a passive method, but it also requires user input. The performance and design assessment of apartments with natural ventilation involves the behavior of the residents (Pal et al, 2022). The training algorithm, network hyper-parameters and input variable selection process are utilized with the artificial neural network (ANN) method to estimate the building's energy conservation (Tarek et al, 2022).…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

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

Sivapriya,

Subbaiyan

2024

IJIUS

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%