Abstract
The paper describes a mid-term daily peak load forecasting method using recurrent artificial neural network (RANN). Generally, the artificial neural network (ANN) algorithm is used to forecast short-term load pattern and many ANN structures have been developed and commercialized so far. Otherwise, learning and estimation for long-term and mid-term load forecasting are hard tasks due to lack of training data and increase of accumulated errors in long period estimation. The paper proposes a mid-term load forecasting structure in order to overcome these problems by input data replacement for special days and a recurrent-type NN application. Also, the proposed RANN gives good performances on estimating sudden and nonlinear demand increase during heat waves. The results of case studies using load data of South Korea are presented to show performances and effectiveness of the proposed RANN.
Highlights
Accurate load forecasting becomes essential for an effective power system management and planning overhauls of the generators in a situation that the power consumption steeply increases and electric power reserve rate becomes insufficient
The recurrent type artificial neural network (ANN) (RANN) is proposed to estimate mid-term load demand in order to overcome these problems with limited input data such as recorded load and temperatures
The input data was selected in order to improve the training and estimating performances and the paper proposed a recurrent artificial neural network (RANN) structure in which the forecasting data was recurrently used as input data
Summary
Accurate load forecasting becomes essential for an effective power system management and planning overhauls of the generators in a situation that the power consumption steeply increases and electric power reserve rate becomes insufficient. Learning and estimation for mid-term load forecasting are hard tasks due to lack of training data and increase of accumulated errors on long period estimation. The recurrent type ANN (RANN) is proposed to estimate mid-term load demand in order to overcome these problems with limited input data such as recorded load and temperatures. The results of case studies using load data of South Korea are presented to show performances and effectiveness of the proposed RANN in comparison with the results of commercial software (KPX short-term load forecaster, KSLF) [17]–[19].
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