Load Forecasting of Green Power System Based on Data Mining

Chun Zhao Zhou; Yan Yan; Hao Zhang; Chao Wang; Liang Wang

doi:10.4028/www.scientific.net/AMM.340.1050

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vol. 340Load Forecasting of Green Power System Based on...

Load Forecasting of Green Power System Based on Data Mining

Abstract:

This project applies Data Mining technology to the prediction of electric power system load forecast. It proposes a mining program of electric power load forecasting data based on the similarity of time series research, in the method, the average of each segment of time-sequence load is used to reduce the dimension of the problem. The similarity inquiring of each sub-sequence of loads is realized by using slipping window and MBR method. The inquiring is improved in efficiency by designing the index structure according to the-tree. Effectively overcome the negative effects on the prediction results caused by the limited and incomplete data. It also illustrates a list of examples to prove that the conducted method is effective and efficient.

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Periodical:

Applied Mechanics and Materials (Volume 340)

Pages:

1050-1054

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.340.1050

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Online since:

July 2013

Authors:

Chun Zhao Zhou, Yan Yan, Hao Zhang, Chao Wang, Liang Wang

Keywords:

Data Mining (DM), Load Forecasting, Power System, Time Series

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References

[1] Jawed H, Michelin K. (2000) Data Mining: Concepts and Techniques Simon Fraser University: Morgan Kaufmann Pub is hers. 1(5), 32-37.

Google Scholar

[2] L. Q. Li (2004) World-wide competition within the EUNITE network EUNITE competition Report, Company behind: East—Slovakia Power Distribution Company. 8(3), 2-9.

Google Scholar

[3] Wei W, Cheep P L, Kook K P (2003) Predicting drug dissolution profiles with an ensemble of boosted neural network: time series approach Idea Trans. on neural networks. 3(2), 459-463.

DOI: 10.1109/tnn.2003.809420

Google Scholar

[4] David Espy (2003) dative Logic Networks for East Slovakian Electrical Load Forecasting. NITE competition Report, Company behind: act-Slovakia Power Distribution Company. 4(5), 73-77.

Google Scholar

[5] Zhou Q., Han P., Zhai Y. (2010) Data processing and experimental research on load forecasting, Computer Engineering and Applications. 5(15), 183-186.

Google Scholar

[6] Muhammad Ritz Khan (2003) Ajith Abraham, Short Term Load Forecasting Models in Czech Republic Using Soft Computing Paradigms, International Journal of Knowledge-Based Intelligent Engineering Systems, IOS Press Netherlands. 6(8), 172-176.

Google Scholar