Energy processes prediction by a convolutional radial basis function network

If an approach based on the gradient steepest descent is utilized to adapt the parameters of a radial basis function network, then it requires dimensionality reduction of the input dataset for the complexity reduction and efficiency improvement, resulting in a more precise energy processes prediction. The convolution operation could provide one way to perform dimensionality reduction of the input dataset. In this research, the convolutional radial basis function network is utilized for the energy processes prediction. The advances are exposed as follows: (1) the convolutional radial basis function network containing a convolution part, a hidden part, and an output part is utilized for the energy processes prediction, (2) the convolution operation is utilized in the convolution part to perform dimensionality reduction of the input dataset, and to change the magnitude of the input dataset for the complexity reduction, (3) the gradient steepest descent is utilized to adapt the parameters in the hidden part and output part for the efficiency improvement. The convolutional radial basis function network is compared against the radial basis function network, the feedforward neural network, and the neuro fuzzy system for the hourly electrical power demand prediction and for the chiller prediction.