Grey Neural Network and Its Application to Short Term Load Forecasting Problem

Cheng-Hsiung HSIEH

Grey Neural Network and Its Application to Short Term Load Forecasting Problem

Cheng-Hsiung HSIEH

Full Text Views

0

Cite this

Summary :

In this paper, a novel type of neural networks called grey neural network (GNN) is proposed and applied to improve short term load forecasting (STLF) performance. This work is motivated by the following observations: First, the forecasting performance of neural network is affected by the randomness in STLF data. That is, poor performance results from large randomness and vice versa. Second, the grey first-order accumulated generating operation (1-AGO) is reported having randomness reduction property. By the observations, the GNN is proposed and expected to have better STLF performance. The GNN consists of grey 1-AGO, the piecewise linear neural network (PLNN), and grey first-order inverse accumulated generating operation (1-IAGO). Given a set of STLF data, the data is first converted by grey 1-AGO and then is put into the PLNN to perform forecasting. Finally, the predicted load of GNN is obtained through grey 1-IAGO. For comparison, the original STLF data is also put into the PLNN itself. With identical training conditions, the simulation results indicate that with various network structures the GNN, as expected, outperforms the PLNN itself in terms of mean squared error.

Publication: IEICE TRANSACTIONS on Information Vol.E85-D No.5 pp.897-902

Publication Date: 2002/05/01

Publicized

Online ISSN

DOI

Type of Manuscript: PAPER

Category: Biocybernetics, Neurocomputing

Cite this

Copy

Cheng-Hsiung HSIEH, "Grey Neural Network and Its Application to Short Term Load Forecasting Problem" in IEICE TRANSACTIONS on Information, vol. E85-D, no. 5, pp. 897-902, May 2002, doi: .
Abstract: In this paper, a novel type of neural networks called grey neural network (GNN) is proposed and applied to improve short term load forecasting (STLF) performance. This work is motivated by the following observations: First, the forecasting performance of neural network is affected by the randomness in STLF data. That is, poor performance results from large randomness and vice versa. Second, the grey first-order accumulated generating operation (1-AGO) is reported having randomness reduction property. By the observations, the GNN is proposed and expected to have better STLF performance. The GNN consists of grey 1-AGO, the piecewise linear neural network (PLNN), and grey first-order inverse accumulated generating operation (1-IAGO). Given a set of STLF data, the data is first converted by grey 1-AGO and then is put into the PLNN to perform forecasting. Finally, the predicted load of GNN is obtained through grey 1-IAGO. For comparison, the original STLF data is also put into the PLNN itself. With identical training conditions, the simulation results indicate that with various network structures the GNN, as expected, outperforms the PLNN itself in terms of mean squared error.
URL: https://global.ieice.org/en_transactions/information/10.1587/e85-d_5_897/_p

Copy

@ARTICLE{e85-d_5_897,
author={Cheng-Hsiung HSIEH, },
journal={IEICE TRANSACTIONS on Information},
title={Grey Neural Network and Its Application to Short Term Load Forecasting Problem},
year={2002},
volume={E85-D},
number={5},
pages={897-902},
abstract={In this paper, a novel type of neural networks called grey neural network (GNN) is proposed and applied to improve short term load forecasting (STLF) performance. This work is motivated by the following observations: First, the forecasting performance of neural network is affected by the randomness in STLF data. That is, poor performance results from large randomness and vice versa. Second, the grey first-order accumulated generating operation (1-AGO) is reported having randomness reduction property. By the observations, the GNN is proposed and expected to have better STLF performance. The GNN consists of grey 1-AGO, the piecewise linear neural network (PLNN), and grey first-order inverse accumulated generating operation (1-IAGO). Given a set of STLF data, the data is first converted by grey 1-AGO and then is put into the PLNN to perform forecasting. Finally, the predicted load of GNN is obtained through grey 1-IAGO. For comparison, the original STLF data is also put into the PLNN itself. With identical training conditions, the simulation results indicate that with various network structures the GNN, as expected, outperforms the PLNN itself in terms of mean squared error.},
keywords={},
doi={},
ISSN={},
month={May},}

Copy

TY - JOUR
TI - Grey Neural Network and Its Application to Short Term Load Forecasting Problem
T2 - IEICE TRANSACTIONS on Information
SP - 897
EP - 902
AU - Cheng-Hsiung HSIEH
PY - 2002
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E85-D
IS - 5
JA - IEICE TRANSACTIONS on Information
Y1 - May 2002
AB - In this paper, a novel type of neural networks called grey neural network (GNN) is proposed and applied to improve short term load forecasting (STLF) performance. This work is motivated by the following observations: First, the forecasting performance of neural network is affected by the randomness in STLF data. That is, poor performance results from large randomness and vice versa. Second, the grey first-order accumulated generating operation (1-AGO) is reported having randomness reduction property. By the observations, the GNN is proposed and expected to have better STLF performance. The GNN consists of grey 1-AGO, the piecewise linear neural network (PLNN), and grey first-order inverse accumulated generating operation (1-IAGO). Given a set of STLF data, the data is first converted by grey 1-AGO and then is put into the PLNN to perform forecasting. Finally, the predicted load of GNN is obtained through grey 1-IAGO. For comparison, the original STLF data is also put into the PLNN itself. With identical training conditions, the simulation results indicate that with various network structures the GNN, as expected, outperforms the PLNN itself in terms of mean squared error.
ER -