TY - JOUR
T1 - Energy processes prediction by a convolutional radial basis function network
AU - Rubio, José de Jesús
AU - Garcia, Donaldo
AU - Sossa, Humberto
AU - Garcia, Ivan
AU - Zacarias, Alejandro
AU - Mujica-Vargas, Dante
N1 - Publisher Copyright:
© 2023 Elsevier Ltd
PY - 2023/12/1
Y1 - 2023/12/1
N2 - 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.
AB - 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.
KW - Convolution operation
KW - Energy processes prediction
KW - Feedforward neural network
KW - Neuro fuzzy system
KW - Radial basis function network
UR - http://www.scopus.com/inward/record.url?scp=85166327683&partnerID=8YFLogxK
U2 - 10.1016/j.energy.2023.128470
DO - 10.1016/j.energy.2023.128470
M3 - Artículo
AN - SCOPUS:85166327683
SN - 0360-5442
VL - 284
JO - Energy
JF - Energy
M1 - 128470
ER -