<abstract xml:lang="en" xmlns="http://www.w3.org/1999/xhtml"><p>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 <italic>etc</italic>. 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.</p></abstract>

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.

Selection of Hidden Layer Neurons and Best Training Method for FFNN in Application of Long Term Load Forecasting

Department of Electrical Engineering, Motilal Nehru National Institute of Technology Allahabad, 211004 India

Journal of Electrical Engineering

For power industries electricity load forecast plays an important role for real-time control, security, optimal unit commitment, economic scheduling,...

Selection of Hidden Layer Neurons and Best Training Method for FFNN in Application of Long Term Load Forecasting

Online veröffentlicht: 30. Juni 2012

Seitenbereich: 153 - 161

DOI: https://doi.org/10.2478/v10187-012-0023-9

Schlüsselwörter
load forecasting, feed forward neural network (FFNN), auto-regressive (AR) method, moving average method

This content is open access.

Selection of Hidden Layer Neurons and Best Training Method for FFNN in Application of Long Term Load Forecasting

Online veröffentlicht: 30. Juni 2012

Seitenbereich: 153 - 161

DOI: https://doi.org/10.2478/v10187-012-0023-9

Schlüsselwörterload forecasting, feed forward neural network (FFNN), auto-regressive (AR) method, moving average method

This content is open access.

Schlüsselwörter
load forecasting, feed forward neural network (FFNN), auto-regressive (AR) method, moving average method