The power load forecasting by kernel PCA

Fang Tsung Liu; Chiung Hsing Chen; Shang Jen Chuang; Ting Chia Ou

doi:10.1007/978-3-642-16732-4_44

The power load forecasting by kernel PCA

Fang Tsung Liu, Chiung Hsing Chen, Shang Jen Chuang, Ting Chia Ou

Program on Smart and Sustainable Manufacturing

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

Abstract

We use one year's subset to train the Support Vector Machines (SVM) and the next year's data was used for testing with Kernel Principal Components Analysis (KPCA). This is clearly not optimal for a non-stationary time series such as we have here nevertheless the MAPE of peak load data set with back-propagation neural network [Chuang et al., 1998] is 3.0 and Support Vector Machine is 2.6.

Original language	English
Title of host publication	Computational Collective Intelligence
Subtitle of host publication	Technologies and Applications - Second International Conference, ICCCI 2010, Proceedings
Pages	411-424
Number of pages	14
Edition	PART 2
DOIs	https://doi.org/10.1007/978-3-642-16732-4_44
Publication status	Published - 2010
Event	2nd International Conference on Computational Collective Intelligence - Technologies and Applications, ICCCI 2010 - Kaohsiung, Taiwan Duration: 2010 Nov 10 → 2010 Nov 12

Publication series

Name	Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Number	PART 2
Volume	6422 LNAI
ISSN (Print)	0302-9743
ISSN (Electronic)	1611-3349

Other

Other	2nd International Conference on Computational Collective Intelligence - Technologies and Applications, ICCCI 2010
Country/Territory	Taiwan
City	Kaohsiung
Period	10-11-10 → 10-11-12

All Science Journal Classification (ASJC) codes

Theoretical Computer Science
General Computer Science

Access to Document

10.1007/978-3-642-16732-4_44

Cite this

Liu, F. T., Chen, C. H., Chuang, S. J., & Ou, T. C. (2010). The power load forecasting by kernel PCA. In Computational Collective Intelligence: Technologies and Applications - Second International Conference, ICCCI 2010, Proceedings (PART 2 ed., pp. 411-424). (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6422 LNAI, No. PART 2). https://doi.org/10.1007/978-3-642-16732-4_44

Liu, Fang Tsung ; Chen, Chiung Hsing ; Chuang, Shang Jen et al. / The power load forecasting by kernel PCA. Computational Collective Intelligence: Technologies and Applications - Second International Conference, ICCCI 2010, Proceedings. PART 2. ed. 2010. pp. 411-424 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2).

@inproceedings{479398cbb7d6452699579ebeb81b2eb7,

title = "The power load forecasting by kernel PCA",

abstract = "We use one year's subset to train the Support Vector Machines (SVM) and the next year's data was used for testing with Kernel Principal Components Analysis (KPCA). This is clearly not optimal for a non-stationary time series such as we have here nevertheless the MAPE of peak load data set with back-propagation neural network [Chuang et al., 1998] is 3.0 and Support Vector Machine is 2.6.",

author = "Liu, {Fang Tsung} and Chen, {Chiung Hsing} and Chuang, {Shang Jen} and Ou, {Ting Chia}",

year = "2010",

doi = "10.1007/978-3-642-16732-4_44",

language = "English",

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series = "Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)",

number = "PART 2",

pages = "411--424",

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note = "2nd International Conference on Computational Collective Intelligence - Technologies and Applications, ICCCI 2010 ; Conference date: 10-11-2010 Through 12-11-2010",

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Liu, FT, Chen, CH, Chuang, SJ & Ou, TC 2010, The power load forecasting by kernel PCA. in Computational Collective Intelligence: Technologies and Applications - Second International Conference, ICCCI 2010, Proceedings. PART 2 edn, Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), no. PART 2, vol. 6422 LNAI, pp. 411-424, 2nd International Conference on Computational Collective Intelligence - Technologies and Applications, ICCCI 2010, Kaohsiung, Taiwan, 10-11-10. https://doi.org/10.1007/978-3-642-16732-4_44

The power load forecasting by kernel PCA. / Liu, Fang Tsung; Chen, Chiung Hsing; Chuang, Shang Jen et al.
Computational Collective Intelligence: Technologies and Applications - Second International Conference, ICCCI 2010, Proceedings. PART 2. ed. 2010. p. 411-424 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 6422 LNAI, No. PART 2).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution

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AU - Chuang, Shang Jen

AU - Ou, Ting Chia

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N2 - We use one year's subset to train the Support Vector Machines (SVM) and the next year's data was used for testing with Kernel Principal Components Analysis (KPCA). This is clearly not optimal for a non-stationary time series such as we have here nevertheless the MAPE of peak load data set with back-propagation neural network [Chuang et al., 1998] is 3.0 and Support Vector Machine is 2.6.

AB - We use one year's subset to train the Support Vector Machines (SVM) and the next year's data was used for testing with Kernel Principal Components Analysis (KPCA). This is clearly not optimal for a non-stationary time series such as we have here nevertheless the MAPE of peak load data set with back-propagation neural network [Chuang et al., 1998] is 3.0 and Support Vector Machine is 2.6.

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Liu FT, Chen CH, Chuang SJ, Ou TC. The power load forecasting by kernel PCA. In Computational Collective Intelligence: Technologies and Applications - Second International Conference, ICCCI 2010, Proceedings. PART 2 ed. 2010. p. 411-424. (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); PART 2). doi: 10.1007/978-3-642-16732-4_44

The power load forecasting by kernel PCA

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