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Pregled bibliografske jedinice broj: 1208287

Dynamic spatio-temporal correlation and hierarchical directed graph structure based ultra- short-term wind farm cluster power forecasting method

Wang, Fei; Chen, Peng; Zhen, Zhao; Yin, Rui; Cao, Chunmei; Zhang, Yagang; Duić, Neven
Dynamic spatio-temporal correlation and hierarchical directed graph structure based ultra- short-term wind farm cluster power forecasting method // Applied energy, 323 (2022), 119579, 30 doi:10.1016/j.apenergy.2022.119579 (međunarodna recenzija, članak, znanstveni)

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Naslov
Dynamic spatio-temporal correlation and hierarchical directed graph structure based ultra- short-term wind farm cluster power forecasting method

Autori
Wang, Fei ; Chen, Peng ; Zhen, Zhao ; Yin, Rui ; Cao, Chunmei ; Zhang, Yagang ; Duić, Neven

Izvornik
Applied energy (0306-2619) 323 (2022); 119579, 30

Vrsta, podvrsta i kategorija rada
Radovi u časopisima, članak, znanstveni

Ključne riječi
Ultra-short-term ; Wind farm cluster power forecasting ; Dynamic spatio-temporal correlation ; Hierarchical directed graph structure ; Causal relationship

Sažetak
Accurate wind farm cluster power forecasting is of great significance for the safe operation of the power system with high wind power penetration. However, most of the current neural network methods used for wind farm cluster power forecasting have the following three problems: (1) lack of consideration of dynamic spatio-temporal correlation among adjacent wind farms ; (2) simultaneously forecasting all wind farms’ power to obtain the total power will produce numerous error sources ; (3) ignoring the causal relationship among input variables. Therefore, to solve the above problems, this paper proposes an ultra-short-term wind farm cluster power forecasting method based on dynamic spatio- temporal correlation and hierarchical directed graph structure. Firstly, three different types of nodes (wind speed nodes, wind power nodes, and target node) and input samples are defined, and then the spatio-temporal correlation matrices that can describe the correlation of adjacent wind farms are also calculated. Secondly, directed edges are defined to connect different nodes in order to obtain the hierarchical directed graph structure. Finally, this graph structure with dynamic spatio-temporal correlation information is used to train the forecasting model. In case study, compared with other benchmark methods, the proposed method shows excellent performance in improving accuracy of power forecasting.

Izvorni jezik
Engleski

Znanstvena područja
Strojarstvo



POVEZANOST RADA

Ustanove:
Fakultet strojarstva i brodogradnje, Zagreb

Profili:

Avatar Url Neven Duić (autor)

Poveznice na cjeloviti tekst rada:

doi www.sciencedirect.com

Citiraj ovu publikaciju:

Wang, Fei; Chen, Peng; Zhen, Zhao; Yin, Rui; Cao, Chunmei; Zhang, Yagang; Duić, Neven
Dynamic spatio-temporal correlation and hierarchical directed graph structure based ultra- short-term wind farm cluster power forecasting method // Applied energy, 323 (2022), 119579, 30 doi:10.1016/j.apenergy.2022.119579 (međunarodna recenzija, članak, znanstveni)
Wang, F., Chen, P., Zhen, Z., Yin, R., Cao, C., Zhang, Y. & Duić, N. (2022) Dynamic spatio-temporal correlation and hierarchical directed graph structure based ultra- short-term wind farm cluster power forecasting method. Applied energy, 323, 119579, 30 doi:10.1016/j.apenergy.2022.119579.
@article{article, author = {Wang, Fei and Chen, Peng and Zhen, Zhao and Yin, Rui and Cao, Chunmei and Zhang, Yagang and Dui\'{c}, Neven}, year = {2022}, pages = {30}, DOI = {10.1016/j.apenergy.2022.119579}, chapter = {119579}, keywords = {Ultra-short-term, Wind farm cluster power forecasting, Dynamic spatio-temporal correlation, Hierarchical directed graph structure, Causal relationship}, journal = {Applied energy}, doi = {10.1016/j.apenergy.2022.119579}, volume = {323}, issn = {0306-2619}, title = {Dynamic spatio-temporal correlation and hierarchical directed graph structure based ultra- short-term wind farm cluster power forecasting method}, keyword = {Ultra-short-term, Wind farm cluster power forecasting, Dynamic spatio-temporal correlation, Hierarchical directed graph structure, Causal relationship}, chapternumber = {119579} }
@article{article, author = {Wang, Fei and Chen, Peng and Zhen, Zhao and Yin, Rui and Cao, Chunmei and Zhang, Yagang and Dui\'{c}, Neven}, year = {2022}, pages = {30}, DOI = {10.1016/j.apenergy.2022.119579}, chapter = {119579}, keywords = {Ultra-short-term, Wind farm cluster power forecasting, Dynamic spatio-temporal correlation, Hierarchical directed graph structure, Causal relationship}, journal = {Applied energy}, doi = {10.1016/j.apenergy.2022.119579}, volume = {323}, issn = {0306-2619}, title = {Dynamic spatio-temporal correlation and hierarchical directed graph structure based ultra- short-term wind farm cluster power forecasting method}, keyword = {Ultra-short-term, Wind farm cluster power forecasting, Dynamic spatio-temporal correlation, Hierarchical directed graph structure, Causal relationship}, chapternumber = {119579} }

Časopis indeksira:


  • Current Contents Connect (CCC)
  • Web of Science Core Collection (WoSCC)
    • Science Citation Index Expanded (SCI-EXP)
    • SCI-EXP, SSCI i/ili A&HCI
  • Scopus

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