This work contributes to the development of a new data-driven method (D-DM) of feedforward neural networks (FNNs) learning.
This work contributes to the development of neural forecasting models with novel randomization-based learning methods.
However, the problem in randomized learning is how to determine the random parameters.
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.
The novelty of this work is the use of pattern representation of the seasonal time series as an alternative to decomposition.
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).
A random vector functional link network (RVFL) is widely used as a universal approximator for classification and regression problems.
In the experimental part of the work the proposed models were used to forecasting the monthly demand for 35 European countries.
Feedforward neural networks with random hidden nodes suffer from a problem with the generation of random weights and biases as these are difficult to set optimally to obtain a good projection space.
The standard method of generating random weights and biases in feedforward neural networks with random hidden nodes, selects them both from the uniform distribution over the same fixed interval.
In this work, a method of random parameters generation for randomized learning of a single-hidden-layer feedforward neural network is proposed.
In this work, a method which adjusts the random parameters, representing the slopes and positions of the sigmoids, to the target function features is proposed.
In these networks the weights and biases of hidden nodes determining the nonlinear feature mapping are set randomly and are not learned.