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Found 8 papers, 4 papers with code
ES-dRNN with Dynamic Attention for Short-Term Load Forecasting
Short-term load forecasting (STLF) is a challenging problem due to the complex nature of the time series expressing multiple seasonality and varying variance.
ES-dRNN: A Hybrid Exponential Smoothing and Dilated Recurrent Neural Network Model for Short-Term Load Forecasting
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.
N-BEATS neural network for mid-term electricity load forecasting
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.
Contextually Enhanced ES-dRNN with Dynamic Attention for Short-Term Load Forecasting
These cells enable the model to capture short-term, long-term and seasonal dependencies across time series as well as to weight dynamically the input information.
Ensemble Forecasting of Monthly Electricity Demand using Pattern Similarity-based Methods
This work presents ensemble forecasting of monthly electricity demand using pattern similarity-based forecasting methods (PSFMs).
A Hybrid Residual Dilated LSTM end Exponential Smoothing Model for Mid-Term Electric Load Forecasting
This work presents a hybrid and hierarchical deep learning model for mid-term load forecasting.
Ensembles of Randomized NNs for Pattern-based Time Series Forecasting
In this work, we propose an ensemble forecasting approach based on randomized neural networks.
Recurrent Neural Networks for Forecasting Time Series with Multiple Seasonality: A Comparative Study
This paper compares recurrent neural networks (RNNs) with different types of gated cells for forecasting time series with multiple seasonality.
