DocumentCode :
233471
Title :
Dynamic forecasting of electric load consumption using adaptive multilayer perceptron(AMLP)
Author :
Lalis, Jeremias T. ; Maravillas, Elmer
Author_Institution :
Coll. of Comput. Studies, Cebu Inst. of Technol. - Univ., Cebu City, Philippines
fYear :
2014
fDate :
12-16 Nov. 2014
Firstpage :
1
Lastpage :
7
Abstract :
Electric energy plays a vital role in the achievement of social, economic and environment development of any nation. Thus, efficient demand planning and production of energy is needed to avoid too much over/under-estimation of electric load. In this study, the researchers proposed a scheme with eight steps for a dynamic time series forecasting using adaptive multilayer perceptron with minimal complexity. Two different data sets; each divided into three overlapping parts (training, validating and testing sets), from two different countries were used in the experiments to measure the robustness and accuracy of the models produced by the AMLP. Experiments results show the effectiveness of the proposed scheme for AMLP in forecasting the electric load consumption based on the calculated coefficient of variance of RMSD, CV(RMSD).
Keywords :
load forecasting; multilayer perceptrons; power consumption; CV-RMSD; RMSD coefficient of variance; adaptive multilayer perceptron; demand planning; dynamic time series forecasting; electric load consumption; energy production; Artificial neural networks; Data models; Forecasting; Multilayer perceptrons; Predictive models; Time series analysis; Training; adaptive multilayer perceptron; backpropagation; electric load consumption; long-term forecasting;
fLanguage :
English
Publisher :
ieee
Conference_Titel :
Humanoid, Nanotechnology, Information Technology, Communication and Control, Environment and Management (HNICEM), 2014 International Conference on
Conference_Location :
Palawan
Print_ISBN :
978-1-4799-4021-9
Type :
conf
DOI :
10.1109/HNICEM.2014.7016237
Filename :
7016237
Link To Document :
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