… The results show that the exploitation of convolutional layers along with LSTM … household
load forecasting. The major contributions of this paper are: (1) developing a hybrid CNN-LSTM …

… Therefore, using a hybrid model with CNN and LSTM to replace LSTM for load forecasting
may not further improve the MAPE. On the contrary, the extra processing might introduce more …

… random household load data, this paper proposes a multi-step short-… load forecasting
method based on the LSTNet. By using CNN to explore local connections between data, LSTM …

… Thus, the results show a 20 to 45% improvement in load forecasts compared to … ] a CNNLSTM
model was developed to asses the load forecasting at the level of an individual household …

… is specialized in sequential data; on the other hand CNN is … Therefore, LSTM and CNN
can be used to extract data … for residential load forecasting such as CNN, LSTM, and CNN-LSTM. …

… paper for short-term load forecasting. The developed method … network (CNN) and long
short-term memory (LSTM) network. … RNN algorithm is implemented for domestic load forecasting …

… of energy intelligent technology, load forecasting technology as an important direction of …
low prediction accuracy, a short-term load forecasting model of multi-scale CNN-LSTM hybrid …

… load forecasting. We performed a grid search over the number of CNN layers, number of
LSTM … accurately predict the future consumption of specific appliances inside the household. …

… -based models, DNN and CNN-RNN. However, due to the … in load forecasting applications,
and LSTM networks should replace them. RNNs and the specific type of their family, LSTM, …

… application for household load forecasting and achieved … household load forecasting,
the proposed method outperforms ARIMA by 19.5%, SVR by 13.1% and classical deep RNN …