Complementary Use of Partial Least-Squares and Artificial Neural Networks for the Annual Electricity Consumption Forecast

Hui Feng Jiang

doi:10.4028/www.scientific.net/AMR.512-515.1113

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 512-515Complementary Use of Partial Least-Squares and...

Complementary Use of Partial Least-Squares and Artificial Neural Networks for the Annual Electricity Consumption Forecast

Abstract:

A model for predicting annual electricity consumption based on the combination of neural network and partial least square method was proposed. The factors affecting the annual electricity consumption are analyzed by means of partial least square method to extract the most important components so that not only the problem of multi-correlation among variables can be solves but also the amount of input dimensions of the neural network can be reduced. Besides, the application of neural network helps to solve the problem of non-linearity of the model. The application example shows that the proposed model has high precision.

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Periodical:

Advanced Materials Research (Volumes 512-515)

Pages:

1113-1116

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.512-515.1113

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Online since:

May 2012

Authors:

Hui Feng Jiang

Keywords:

Annual Electricity Consumption, Artificial Neural Network (ANN), Partial Least Square (PLS)

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References

[1] Héctor C. Goicoechea · María S. Collad María L. Satuf · Alejandro C. Olivieri: Complementary use of partial least-squares and artificial neural networks for the non-linear spectrophotometric analysis of pharmaceutical samples, Anal Bioanal Chem (2002) 374:460–465.

DOI: 10.1007/s00216-002-1435-3

Google Scholar

[2] Nanxiang Chen, Qiang Huang, Lianhai Cao: Model for Prediction of Karst Spring Flow based on the Coupling of Neural Network Model with Partial Least Square Method. Journal of Hydraulic Engineering, 2004(9), 1-6. In Chinese.

Google Scholar

[3] Guoqiang PZ: An investigation of neural network for linear time-series forecasting. Computers &Operations Research, 2001(28), 1183—1202.

DOI: 10.1016/s0305-0548(00)00033-2

Google Scholar

[4] Information on http://www.jssb.gov.cn/.

Google Scholar