The Potential of Biomass Energy from Agricultural Residues for Small Electricity Generation in Thailand

Panklib, T.; Prakasvudhisarn, Chakguy; Khummongkol, D.

doi:10.1080/15567036.2010.545809

Cited by 5 publications

(1 citation statement)

References 2 publications

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“…To support economic growth and meet power requirements continually in the future, electricity consumption forecasting has become a very important task for electric utilities. In Thailand, electricity consumption and peak electric power demand grew at a rate of 7.0% and 7.5% per annum during 1989 to 2008 (Panklib et al, 2014). Electric consumption will continue to increase in the power system.…”

Section: Introductionmentioning

confidence: 96%

Electricity Consumption Forecasting in Thailand Using an Artificial Neural Network and Multiple Linear Regression

Panklib

Prakasvudhisarn

Khummongkol

2015

Energy Sources, Part B: Economics, Planning, and Policy

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In this article, an artificial neural network (ANN) and a regression model are applied to forecast long term electricity consumption in Thailand. The inputs of both nonlinear models are gross domestic product, number of population. Maximum ambient temperature and electricity power demand are used as inputs in a neural network to predict electricity consumption. The results show that the ANN model can give more accurate estimations than regression model as indicated by the performance measures, namely coefficient of determination, mean absolute percentage error and root mean square error. Accoding to the forecasting results by the regression and ANN models of this study, the electricity consumption of the country in 2010, 2015, and 2020 will reach 160,136, 188,552, and 216,986 GWh, respectively, for the regression model while the ANN model will reach 155,917, 174,394, and 188,137 GWh, respectively.

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Section: Introductionmentioning

confidence: 96%