Day-Ahead Price Forecasting in Asia's First Liberalised Electricity Market Using Artificial Neural Networks

DOI

10.2991/ijcis.2011.4.4.7How to use a DOI?

Keywords

Price forecasting, Levenberg-marquardt (LM) algorithm, Generalized regression neural network (GRNN), Cascade-forward neural network (CFNN), National electricity market of singapore (NEMS), Uniform singapore energy price (USEP)

Abstract

This paper proposes a comparative model for the day-ahead electricity price forecasting that could be realized using multi-layer neural network (MLNN) with levenberg-marquardt (LM) algorithm, generalized regression neural network (GRNN) and cascade-forward neural network (CFNN). In this work applications of various models were applied to national electricity market of Singapore (NEMS), i.e. Asia's first liberalized electricity market. The individual price of year 2006 is very volatile with a very wide range. Therefore, accurate forecasting models are required for Singapore electricity market company (EMC) to maximize their profits and for consumers to maximize their utilities. Hence the year 2006 has been taken for forecasting the uniform Singapore electricity price (USEP). The mean absolute percentage error (MAPE) results show that the proposed CFNN model possess better forecasting abilities than the other models and its performance was least affected by the volatility.

Copyright

© 2011, the Authors. Published by Atlantis Press.

Open Access

This is an open access article distributed under the CC BY-NC license (http://creativecommons.org/licenses/by-nc/4.0/).

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TY  - JOUR
AU  - S. Anbazhagan
AU  - N. Kumarappan
PY  - 2011
DA  - 2011/06/01
TI  - Day-Ahead Price Forecasting in Asia's First Liberalised Electricity Market Using Artificial Neural Networks
JO  - International Journal of Computational Intelligence Systems
SP  - 476
EP  - 485
VL  - 4
IS  - 4
SN  - 1875-6883
UR  - https://doi.org/10.2991/ijcis.2011.4.4.7
DO  - 10.2991/ijcis.2011.4.4.7
ID  - Anbazhagan2011
ER  -