Short-Term Load Forecasting for Smart Home Appliances with Sequence to Sequence Learning Article Swipe

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Mina Razghandi , Hao Zhou , Melike Erol‐Kantarci , Damla Turgut ·

YOU? · · 2021 · Open Access · · DOI: https://doi.org/10.1109/icc42927.2021.9500767

Appliance-level load forecasting plays a critical role in residential energy management, besides having significant importance for ancillary services performed by the utilities. In this paper, we propose to use an LSTM-based sequence-to-sequence (seq2seq) learning model that can capture the load profiles of appliances. We use a real dataset collected fromfour residential buildings and compare our proposed schemewith three other techniques, namely VARMA, Dilated One Dimensional Convolutional Neural Network, and an LSTM model.The results show that the proposed LSTM-based seq2seq model outperforms other techniques in terms of prediction error in most cases.

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Computer Science

Convolutional Neural Network

Artificial Intelligence

Recurrent Neural Network

Time Series

Deep Learning

Long Short-Term Memory

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Sequence (biology) Term (time) Computer science Convolutional neural network Artificial intelligence Recurrent neural network Time sequence Deep learning Energy (signal processing) Long short term memory Machine learning Sequence learning Artificial neural network Statistics Mathematics Biology Quantum mechanics Physics Genetics

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Type: preprint
Language: en
Landing Page: https://doi.org/10.1109/icc42927.2021.9500767
OA Status: green
Cited By: 3
References: 21
Related Works: 20
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DOI: https://doi.org/10.1109/icc42927.2021.9500767

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Title: Short-Term Load Forecasting for Smart Home Appliances with Sequence to Sequence Learning

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Type: preprint

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Language: en

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Publication year: 2021

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Publication date: 2021-06-01

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Authors: Mina Razghandi, Hao Zhou, Melike Erol‐Kantarci, Damla Turgut

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Landing page: https://doi.org/10.1109/icc42927.2021.9500767

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Concepts: Sequence (biology), Term (time), Computer science, Convolutional neural network, Artificial intelligence, Recurrent neural network, Time sequence, Deep learning, Energy (signal processing), Long short term memory, Machine learning, Sequence learning, Artificial neural network, Statistics, Mathematics, Biology, Quantum mechanics, Physics, Genetics

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Cited by: 3

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Citations by year (recent): 2024: 1, 2022: 1, 2021: 1

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Related works (count): 20

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sustainable_development_goals[0].score	0.8700000047683716
sustainable_development_goals[0].display_name	Affordable and clean energy
citation_normalized_percentile.value	0.56633428
citation_normalized_percentile.is_in_top_1_percent	False
citation_normalized_percentile.is_in_top_10_percent	False