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

• HSIEH Cheng-Hsiuug

  Department of Electronic Engineering of Chien Kuo Institute of Technology

Abstract

In this paper, a novel type of neural networks called grey nenral 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 oboervations, 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.

Journal

• IEICE Trans. Inf. Syst., D

  IEICE Trans. Inf. Syst., D 85 (5), 897-902, 2002-05-01

  The Institute of Electronics, Information and Communication Engineers

Keywords

• grey 1-AGO
• piecewise linear neural network (PLNN)
• short term load forecasting (STLF)
• grey neural network

Details

• CRID: 1572824502325511552
• NII Article ID: 110003210637
• NII Book ID: AA10826272
• ISSN: 09168532
• Text Lang: en
• Data Source:
  • CiNii Articles