Short-Term Load Forecasting for Smart HomeAppliances with Sequence to Sequence Learning
This work addresses appliance-level load forecasting for residential energy management and utilities, but it is incremental as it applies a known seq2seq approach to a specific domain.
The paper tackled short-term load forecasting for smart home appliances by proposing an LSTM-based sequence-to-sequence learning model, which outperformed VARMA, dilated 1D CNN, and LSTM models in prediction error on a real dataset from four residential buildings.
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.