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

Abstract

Purpose

This 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/approach

An 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.

Findings

The MASE measure of the proposed model was 52.55, 43.83, 42.01 and 36.81% higher than ANN, CNN, DTCN and 1DCNN.

Originality/value

The findings of the suggested model in residences were attained high-quality measures with high accuracy, precision and variance.

Citation

Sivapriya, C. and Subbaiyan, G. (2024), "Analyzing the opening and closing of windows in residential for predicting the energy consumption using optimized multi-scale convolution networks", International Journal of Intelligent Unmanned Systems, Vol. 12 No. 3, pp. 245-269. https://doi.org/10.1108/IJIUS-06-2023-0059