SELECTION OF HIDDEN LAYER NEURONS AND BEST TRAINING METHOD FOR FFNN IN APPLICATION OF LONG TERM LOAD FORECASTING

被引：8

作者：

Singh, Navneet K. ^{[1
]}

Singh, Asheesh K. ^{[1
]}

Tripathy, Manoj ^{[1
]}

机构：

[1] Motilal Nehru Natl Inst Technol, Dept Elect Engn, Allahabad 211004, Uttar Pradesh, India

来源：

JOURNAL OF ELECTRICAL ENGINEERING-ELEKTROTECHNICKY CASOPIS | 2012年 / 63卷 / 03期

关键词：

load forecasting; feed forward neural network (FFNN); auto-regressive (AR) method; moving average method; NEURAL-NETWORK; PREDICTION; MODEL;

D O I：

10.2478/v10187-012-0023-9

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

For power industries electricity load forecast plays an important role for real-time control, security, optimal unit commitment, economic scheduling, maintenance, energy management, and plant structure planning etc. A new technique for long term load forecasting (LTLF) using optimized feed forward artificial neural network (FFNN) architecture is presented in this paper, which selects optimal number of neurons in the hidden layer as well as the best training method for the case study. The prediction performance of proposed technique is evaluated using mean absolute percentage error (MAPE) of Thailand private electricity consumption and forecasted data. The results obtained are compared with the results of classical auto-regressive (AR) and moving average (MA) methods. It is, in general, observed that the proposed method is prediction wise more accurate.

引用

页码：153 / 161

页数：9

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