Instruct: Identify the main and secondary category of Arxiv papers based on the titles and abstracts
Query: Short-Term Load Forecasting for Smart HomeAppliances with Sequence to
  Sequence Learning   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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The categories of this paper are: dg-ga
11
The categories of this paper are: hep-lat
30
The categories of this paper are: q-alg
2
The categories of this paper are: math
32
The categories of this paper are: atom-ph
10
The categories of this paper are: q-bio
21
The categories of this paper are: adap-org
4
The categories of this paper are: cond-mat
6
The categories of this paper are: astro-ph
27
The categories of this paper are: comp-gas
13
The categories of this paper are: quant-ph
22
The categories of this paper are: chao-dyn
16
The categories of this paper are: stat
37
The categories of this paper are: supr-con
7
The categories of this paper are: hep-th
23
The categories of this paper are: solv-int