M. Razghandi, H. Zhou, M. Erol-Kantarci, and D. Turgut

Short-Term Load Forecasting for Smart Home Appliances With Sequence to Sequence Learning


Cite as:

M. Razghandi, H. Zhou, M. Erol-Kantarci, and D. Turgut. Short-Term Load Forecasting for Smart Home Appliances With Sequence to Sequence Learning. In Proc. of IEEE ICC 2021, June 2021.

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Abstract:

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 from four residential buildings and compare our proposed scheme with 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.

BibTeX:

@inproceedings{Razghandi-2021-ICC,
	author = "M. Razghandi and H. Zhou and M. Erol-Kantarci and D. Turgut",
	title = "Short-Term Load Forecasting for Smart Home Appliances With Sequence to Sequence Learning",
	booktitle = "Proc. of IEEE ICC 2021",
	year = "2021",
	month = "June",
	abstract = {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 from four residential buildings and compare our proposed scheme with 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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