Energy Consumption Prediction Model for Smart Homes via Decentralized Federated Learning With LSTM

Połap, Dawid; Srivastava, Gautam; Jaszcz, Antoni

doi:10.1109/tce.2023.3325941

Cited by 2 publications

(1 citation statement)

References 21 publications

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“…E-noses are increasingly studied and they have been widely used in various fields, such as the food industry, agriculture, healthcare, air pollution monitoring, and security systems [2]. Various sensor applications utilize machine learning algorithms for specific purposes [3], and these algorithms are also used to process multiple time-varying sensor signals generated in E-noses to quantify and identify target gases. Initially, E-nose algorithms employed linear techniques, such as principal component analysis and partial least squares regression [2,4,5], or nonlinear techniques such as support vector machines [2,6].…”

Section: Introductionmentioning

confidence: 99%

A Sensor Drift Compensation Method with a Masked Autoencoder Module

Kwon,

Park,

Jang

et al. 2024

Applied Sciences

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Deep learning algorithms are widely used for pattern recognition in electronic noses, which are sensor arrays for gas mixtures. One of the challenges of using electronic noses is sensor drift, which can degrade the accuracy of the system over time, even if it is initially trained to accurately estimate concentrations from sensor data. In this paper, an effective drift compensation method is introduced that adds sensor drift information during training of a neural network that estimates gas concentrations. This is achieved by concatenating a calibration feature vector with sensor data and using this as an input to the neural network. The calibration feature vector is generated via a masked-autoencoder-based feature extractor trained with transfer samples, and acts as a prompt to convey sensor drift information. Our method is tested on a 3-year gas sensor array drift dataset, showing that a neural network using our method performs better than other models, including a network with additional fine tuning, demonstrating that our method is efficient at compensating for sensor drift. In this study, the effectiveness of using prompts for network training is confirmed, which better compensates for drifts in new sensor signals than network fine-tuning.

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