The Analysis and Predication of Energy Use in Smart Homes Based on Machine Learning

Xiong, Xuantang; Wei, Yanji

doi:10.1109/cds49703.2020.00080

Cited by 3 publications

(1 citation statement)

References 9 publications

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“…Another aspect of this category is the analysis and prediction of energy use in smart homes using machine learning models. One of the pioneering works in this category is by Xiong [26], which analyzes and predicts energy use in smart homes using machine learning models. Dlamini [27] proposed a deep quantile regression model and a gradient boosting model for power consumption forecasting.…”

Section: Related Workmentioning

confidence: 99%

Predicting Residential Electricity Consumption Using CNN-BiLSTM-SA Neural Networks

Wu,

2024

IEEE Access

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As global population growth and the use of household appliances increase, residential electricity consumption has surged, leading to challenges in maintaining a balanced electrical load. This surge often results in localized and intermittent power outages, adversely affecting residential electricity reliability and the profitability of power supply companies. Addressing this, we propose a novel CNN-BiLSTM-SA model, combining Convolutional Neural Network (CNN), Bidirectional Long Short-Term Memory (BiLSTM), and Self-Attention (SA) mechanisms, to accurately predict residential electricity consumption. The model integrates temporal feature extraction through the CNN module, correlation capture via BiLSTM, and intrinsic feature analysis using SA mechanisms, enhancing predictive accuracy. Our experimental results demonstrate the model's precision, outperforming existing methods in key performance metrics. Additionally, ablation studies affirm the synergistic design of CNN-BiLSTM-SA's network components, contributing to its overall efficacy.

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Section: Related Workmentioning

confidence: 99%

Predicting Residential Electricity Consumption Using CNN-BiLSTM-SA Neural Networks

Wu,

2024

IEEE Access

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Analytical study of machine learning techniques on the smart home energy consumption

Singh,

Solanki,

Sharma

2023

AIP Conference Proceedings

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A Compact High-Gain 28 GHz Antenna Array for Beyond 5G Wireless Networks

Meates,

Alkaraki,

Aslam

et al. 2024

2024 18th European Conference on Antennas and Propagation (EuCAP)

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T01 Sub-6 GHz for terrestrial networks (5G/6G) T02 Mm-wave for terrestrial networks 5G/6G T03 Aerospace, new space and non-terrestrial networks T04 RF sensing for automotive, security, IoT, and other applications T05 Positioning, localization, identification & tracking T06 Biomedical and health T07 THz and high frequency technologies T08 EM modelling and simulation tools T09 Fundamental research and emerging technologies T10 Novel materials, metamaterials, metasurfaces and manufacturing processes Time

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The Analysis and Predication of Energy Use in Smart Homes Based on Machine Learning

Cited by 3 publications

References 9 publications

Predicting Residential Electricity Consumption Using CNN-BiLSTM-SA Neural Networks

Predicting Residential Electricity Consumption Using CNN-BiLSTM-SA Neural Networks

Analytical study of machine learning techniques on the smart home energy consumption

A Compact High-Gain 28 GHz Antenna Array for Beyond 5G Wireless Networks

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