The hybrid model based on Hilbert-Huang transform and neural networks for forecasting of short-term operation conditions of power system

V. G. Kurbatsky; D. N. Sidorov; V. A. Spiryaev; N. V. Tomin

doi:10.1109/PTC.2011.6019155

The hybrid model based on Hilbert-Huang transform and neural networks for forecasting of short-term operation conditions of power system

V. G. Kurbatsky^*
, D. N. Sidorov
, V. A. Spiryaev
, N. V. Tomin

^*Corresponding author for this work

Melent'ev Institute of Power Engineering Systems

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

16 Scopus citations

Abstract

The paper addresses the conventional approaches to the short-term forecasting of nonstationary processes in complex power systems using the methodology of artificial neural networks (ANNs). In many practical cases the application of different ANNs can provide a satisfactory forecast. But data preprocessing and analysis can significantly improve the forecast. In this paper the Hilbert-Huang Transform (HHT) is used as one of the most promising tools in this area. Here we focus on HHT since this transform underlies the proposed two-stage intelligent approach to short-term forecasting of nonstationary processes.

Original language	English
Title of host publication	2011 IEEE PES Trondheim PowerTech
Subtitle of host publication	The Power of Technology for a Sustainable Society, POWERTECH 2011
DOIs	https://doi.org/10.1109/PTC.2011.6019155
State	Published - 2011
Externally published	Yes
Event	2011 IEEE PES Trondheim PowerTech: The Power of Technology for a Sustainable Society, POWERTECH 2011 - Trondheim, Norway Duration: 19 Jun 2011 → 23 Jun 2011

Publication series

Name	2011 IEEE PES Trondheim PowerTech: The Power of Technology for a Sustainable Society, POWERTECH 2011

Conference

Conference	2011 IEEE PES Trondheim PowerTech: The Power of Technology for a Sustainable Society, POWERTECH 2011
Country/Territory	Norway
City	Trondheim
Period	19/06/11 → 23/06/11

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Hilbert-Huang Transform
artificial neural networks
forecasting
hybrid model
operation conditions
power system

Access to Document

10.1109/PTC.2011.6019155

Cite this

Kurbatsky, V. G., Sidorov, D. N., Spiryaev, V. A., & Tomin, N. V. (2011). The hybrid model based on Hilbert-Huang transform and neural networks for forecasting of short-term operation conditions of power system. In 2011 IEEE PES Trondheim PowerTech: The Power of Technology for a Sustainable Society, POWERTECH 2011 Article 6019155 (2011 IEEE PES Trondheim PowerTech: The Power of Technology for a Sustainable Society, POWERTECH 2011). https://doi.org/10.1109/PTC.2011.6019155

Kurbatsky, V. G. ; Sidorov, D. N. ; Spiryaev, V. A. et al. / The hybrid model based on Hilbert-Huang transform and neural networks for forecasting of short-term operation conditions of power system. 2011 IEEE PES Trondheim PowerTech: The Power of Technology for a Sustainable Society, POWERTECH 2011. 2011. (2011 IEEE PES Trondheim PowerTech: The Power of Technology for a Sustainable Society, POWERTECH 2011).

@inproceedings{04e55e7543614f9a9e3b0f903ffdaf96,

title = "The hybrid model based on Hilbert-Huang transform and neural networks for forecasting of short-term operation conditions of power system",

abstract = "The paper addresses the conventional approaches to the short-term forecasting of nonstationary processes in complex power systems using the methodology of artificial neural networks (ANNs). In many practical cases the application of different ANNs can provide a satisfactory forecast. But data preprocessing and analysis can significantly improve the forecast. In this paper the Hilbert-Huang Transform (HHT) is used as one of the most promising tools in this area. Here we focus on HHT since this transform underlies the proposed two-stage intelligent approach to short-term forecasting of nonstationary processes.",

keywords = "Hilbert-Huang Transform, artificial neural networks, forecasting, hybrid model, operation conditions, power system",

author = "Kurbatsky, \{V. G.\} and Sidorov, \{D. N.\} and Spiryaev, \{V. A.\} and Tomin, \{N. V.\}",

year = "2011",

doi = "10.1109/PTC.2011.6019155",

language = "英语",

isbn = "9781424484195",

series = "2011 IEEE PES Trondheim PowerTech: The Power of Technology for a Sustainable Society, POWERTECH 2011",

booktitle = "2011 IEEE PES Trondheim PowerTech",

note = "2011 IEEE PES Trondheim PowerTech: The Power of Technology for a Sustainable Society, POWERTECH 2011 ; Conference date: 19-06-2011 Through 23-06-2011",

}

Kurbatsky, VG, Sidorov, DN, Spiryaev, VA & Tomin, NV 2011, The hybrid model based on Hilbert-Huang transform and neural networks for forecasting of short-term operation conditions of power system. in 2011 IEEE PES Trondheim PowerTech: The Power of Technology for a Sustainable Society, POWERTECH 2011., 6019155, 2011 IEEE PES Trondheim PowerTech: The Power of Technology for a Sustainable Society, POWERTECH 2011, 2011 IEEE PES Trondheim PowerTech: The Power of Technology for a Sustainable Society, POWERTECH 2011, Trondheim, Norway, 19/06/11. https://doi.org/10.1109/PTC.2011.6019155

The hybrid model based on Hilbert-Huang transform and neural networks for forecasting of short-term operation conditions of power system. / Kurbatsky, V. G.; Sidorov, D. N.; Spiryaev, V. A. et al.
2011 IEEE PES Trondheim PowerTech: The Power of Technology for a Sustainable Society, POWERTECH 2011. 2011. 6019155 (2011 IEEE PES Trondheim PowerTech: The Power of Technology for a Sustainable Society, POWERTECH 2011).

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

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AU - Kurbatsky, V. G.

AU - Sidorov, D. N.

AU - Spiryaev, V. A.

AU - Tomin, N. V.

PY - 2011

Y1 - 2011

N2 - The paper addresses the conventional approaches to the short-term forecasting of nonstationary processes in complex power systems using the methodology of artificial neural networks (ANNs). In many practical cases the application of different ANNs can provide a satisfactory forecast. But data preprocessing and analysis can significantly improve the forecast. In this paper the Hilbert-Huang Transform (HHT) is used as one of the most promising tools in this area. Here we focus on HHT since this transform underlies the proposed two-stage intelligent approach to short-term forecasting of nonstationary processes.

AB - The paper addresses the conventional approaches to the short-term forecasting of nonstationary processes in complex power systems using the methodology of artificial neural networks (ANNs). In many practical cases the application of different ANNs can provide a satisfactory forecast. But data preprocessing and analysis can significantly improve the forecast. In this paper the Hilbert-Huang Transform (HHT) is used as one of the most promising tools in this area. Here we focus on HHT since this transform underlies the proposed two-stage intelligent approach to short-term forecasting of nonstationary processes.

KW - Hilbert-Huang Transform

KW - artificial neural networks

KW - forecasting

KW - hybrid model

KW - operation conditions

KW - power system

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Kurbatsky VG, Sidorov DN, Spiryaev VA, Tomin NV. The hybrid model based on Hilbert-Huang transform and neural networks for forecasting of short-term operation conditions of power system. In 2011 IEEE PES Trondheim PowerTech: The Power of Technology for a Sustainable Society, POWERTECH 2011. 2011. 6019155. (2011 IEEE PES Trondheim PowerTech: The Power of Technology for a Sustainable Society, POWERTECH 2011). doi: 10.1109/PTC.2011.6019155

The hybrid model based on Hilbert-Huang transform and neural networks for forecasting of short-term operation conditions of power system

Abstract

Publication series

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UN SDGs

Keywords

Access to Document

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