This paper compares recurrent neural networks (RNNs) with different types of gated cells for forecasting time series with multiple seasonality.
Short-term load forecasting (STLF) is a challenging problem due to the complex nature of the time series expressing multiple seasonality and varying variance.
A multi-layer RNN is equipped with a new type of dilated recurrent cell designed to efficiently model both short and long-term dependencies in TS.
We show that our proposed deep neural network modeling approach based on the deep neural architecture is effective at solving the mid-term electricity load forecasting problem.
This work presents a hybrid and hierarchical deep learning model for mid-term load forecasting.
This work presents ensemble forecasting of monthly electricity demand using pattern similarity-based forecasting methods (PSFMs).