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Load Forecasting Research Based on High Performance Intelligent Data Processing of Power Big Data

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Chong WangAuthors Info & Claims

ICACS '18: Proceedings of the 2nd International Conference on Algorithms, Computing and Systems

Pages 55 - 60

https://doi.org/10.1145/3242840.3242842

Published: 27 July 2018 Publication History

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Abstract

The method proposed in this paper is a data analysis method that intelligently analyzes power big data and realizes the load forecasting of power grid. The method calls for the corresponding data from each database of big data platform by accepting the load forecast request from the client, and performs the load forecasting in the big data by improving the gray model of chaos genetic algorithm (CGA). After the completion of load forecasting, the final output to the client load forecasting results.

References

[1]

Bow Javed, F., Arshad, N., Wallin, F.: Forecasting for demand response in smart grids: An analysis on use of anthropologic and structural data and short term multiple loads forecasting. Applied Energy. 96, 150--160(2012).

Crossref

Google Scholar

[2]

Borges, C. E., Penya, Y. K.: Fernandez I. Evaluating combined load forecasting in large power systems and smart grids. IEEE Transactions on Industrial Informatics. 9(3), 1570--1577 (2013).

Crossref

Google Scholar

[3]

Beccali, M., Cellura, M., Brano, V. L.: Forecasting daily urban electric load profiles using artificial neural networks. Energy conversion and management. 45(18), 2879--2900(2004).Ding, W. and Marchionini, G. 1997. A Study on Video Browsing Strategies. Technical Report. University of Maryland at College Park.

Google Scholar

[4]

Hagan, M.T., Behr, S.M.: The Time Series Approach to Short Term Load Forecasting. IEEE Transactions on Power Systems, 2(3), 785--791(1987)

Crossref

Google Scholar

[5]

Papalexopoulos, A. D., Hesterberg, T. C.: A regression-based approach to short-term system load forecasting. IEEE Transactions on Power Systems. 5(4), 1535--1547(1990).

Crossref

Google Scholar

[6]

Niu, D.: Adjustment gray model for load forecasting of power systems. IEEE Trans. on PWRS. 5(4), 1535W-1547(1990).

Google Scholar

[7]

Chow, T. W. S., C. T. Leung.: Neural network based short-term load forecasting using weather compensation. IEEE Transactions on Power Systems. 11(4), 1736--1742(1996).

Crossref

Google Scholar

[8]

Yu, S., Zhu, K., Diao, F.: A dynamic all parameters adaptive BP neural networks model and its application on oil reservoir prediction. Applied mathematics and computation. 195(1), 66--75(2008).

Google Scholar

[9]

He, Y., Xu, Q.: Short-term power load forecasting based on self-adapting PSO-BP neural network model. In: Computational and Information Sciences (ICCIS), 2012 Fourth International Conference on. IEEE. 1096--1099(2012).

Digital Library

Google Scholar

[10]

Wang, H., Shan, G., Duan, X.: Optimization of LM-BP neural network algorithm for analog circuit fault diagnosis. In: Information Science and Technology (ICIST), pp, 271--274. 2013 International Conference on. IEEE, Karnataka (2013).

Google Scholar

[11]

Google Scholar

[12]

De Comité, F., Gilleron, R., Tommasi, M.: Learning multi-label alternating decision trees from texts and data. In: International Workshop on Machine Learning and Data Mining in Pattern Recognition, pp, 35--49. Springer, Berlin Heidelberg(2003).

Digital Library

Google Scholar

[13]

Frean, M.: The upstart algorithm: A method for constructing and training feedforward neural networks. Neural computation. 2(2), 198--209(1990).

Digital Library

Google Scholar

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Liu JYin Y(2022)Power Load Forecasting Considering Climate Factors Based on IPSO-Elman Method in ChinaEnergies10.3390/en1503123615:3(1236)Online publication date: 8-Feb-2022
https://doi.org/10.3390/en15031236
Alamaniotis M(2022)Multi-kernel Analysis Method for Intelligent Data Processing with Application to Prediction MakingIntelligent Decision Technologies10.1007/978-981-19-3444-5_25(279-288)Online publication date: 27-Jul-2022
https://doi.org/10.1007/978-981-19-3444-5_25
Rabie AAli SSaleh AAli H(2019)A fog based load forecasting strategy based on multi-ensemble classification for smart gridsJournal of Ambient Intelligence and Humanized Computing10.1007/s12652-019-01299-x11:1(209-236)Online publication date: 20-Apr-2019
https://doi.org/10.1007/s12652-019-01299-x

Index Terms

Load Forecasting Research Based on High Performance Intelligent Data Processing of Power Big Data
1. Information systems
  1. Information systems applications
    1. Decision support systems
      1. Data analytics

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In order to efficiently improve the prediction accuracy by selecting input variables and the training pattern,a load forecasting model based on data mining technique is presented. The model consists of three stages:firstly,the rough set theory and the ...

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ICACS '18: Proceedings of the 2nd International Conference on Algorithms, Computing and Systems

July 2018

245 pages

ISBN:9781450365093

DOI:10.1145/3242840

Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Xi'an Jiaotong-Liverpool University: Xi'an Jiaotong-Liverpool University

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Published: 27 July 2018

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ICACS '18: 2018 2nd International Conference on Algorithms, Computing and Systems

July 27 - 29, 2018

Beijing, China

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Liu JYin Y(2022)Power Load Forecasting Considering Climate Factors Based on IPSO-Elman Method in ChinaEnergies10.3390/en1503123615:3(1236)Online publication date: 8-Feb-2022
https://doi.org/10.3390/en15031236
Alamaniotis M(2022)Multi-kernel Analysis Method for Intelligent Data Processing with Application to Prediction MakingIntelligent Decision Technologies10.1007/978-981-19-3444-5_25(279-288)Online publication date: 27-Jul-2022
https://doi.org/10.1007/978-981-19-3444-5_25
Rabie AAli SSaleh AAli H(2019)A fog based load forecasting strategy based on multi-ensemble classification for smart gridsJournal of Ambient Intelligence and Humanized Computing10.1007/s12652-019-01299-x11:1(209-236)Online publication date: 20-Apr-2019
https://doi.org/10.1007/s12652-019-01299-x

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Processing Big Data with Azure HDInsight: Building Real-World Big Data Systems on Azure HDInsight Using the Hadoop Ecosystem

Big Data Analytics

Research on Power System Load Forecasting Model Based on Data Mining Technology

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Processing Big Data with Azure HDInsight: Building Real-World Big Data Systems on Azure HDInsight Using the Hadoop Ecosystem

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