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dc.contributor.authorV. Sackdaraen_US
dc.contributor.authorS. Premrudeepreechacharnen_US
dc.contributor.authorK. Ngamsanroajen_US
dc.description.abstractElectricity is one of not only the most necessities for the daily life activities of people, but also the major driving force for economic growth and development of every country. Due to the unstorable nature of electricity, the adequate supply of electricity has to be always available and uninterruptible to meet the intermittently growing demand. This paper is proposed Neural Networks (NN) with Backpropagation learning algorithm and regression analysis approaches for electricity demand forecasting. We aim to compare these two methods in this paper using the mean absolute percentage error (MAPE) to measure the forecasting performance. The factors that, number of population, number of household, electricity price and gross domestic product (GDP) are selected based on correlation coefficients. The results show that neural networks model is more effective than regression analysis model. © 2010 IEEE.en_US
dc.subjectComputer Scienceen_US
dc.titleElectricity demand forecasting of Electricité Du Lao (EDL) using Neural Networksen_US
dc.typeConference Proceedingen_US
article.title.sourcetitleIEEE Region 10 Annual International Conference, Proceedings/TENCONen_US Mai Universityen_US Generating Authority of Thailand (EGAT)en_US
Appears in Collections:CMUL: Journal Articles

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