Details
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 2022 Winter Simulation Conference, WSC 2022 |
| Editors | B. Feng, G. Pedrielli, Y. Peng, S. Shashaani, E. Song, C.G. Corlu, L.H. Lee, E.P. Chew, T. Roeder, P. Lendermann |
| Publisher | Institute of Electrical and Electronics Engineers Inc. |
| Pages | 760-771 |
| Number of pages | 12 |
| ISBN (electronic) | 9798350309713, 978-1-6654-7661-4 |
| ISBN (print) | 978-1-6654-7662-1 |
| Publication status | Published - 2022 |
| Event | 2022 Winter Simulation Conference, WSC 2022 - , Singapore Duration: 11 Dec 2022 → 14 Dec 2022 |
Publication series
| Name | Proceedings - Winter Simulation Conference |
|---|---|
| Volume | 2022-December |
| ISSN (Print) | 0891-7736 |
| ISSN (electronic) | 1558-4305 |
Abstract
As an important part of renewable energy resources, offshore wind energy has great potential compared to its onshore counterpart despite the vast developments in recent decades. However, due to the more complex environmental condition and physical restrictions the installation of an offshore wind farm is hard to plan and predict, which often results in delays. This paper focuses on the scheduling problem in the installation phase of an offshore wind farm. We propose an adaptive search strategy based on the Apriori property and information entropy. The purpose is to prune the search space effectively and intelligently to realize an agile and swift rescheduling according to the environmental changes. For the numerical experiments, we use the environmental data obtained from the German North Sea from the year 1958 to 2007 in hourly resolution.
ASJC Scopus subject areas
- Computer Science(all)
- Software
- Mathematics(all)
- Modelling and Simulation
- Computer Science(all)
- Computer Science Applications
Sustainable Development Goals
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Proceedings of the 2022 Winter Simulation Conference, WSC 2022. ed. / B. Feng; G. Pedrielli; Y. Peng; S. Shashaani; E. Song; C.G. Corlu; L.H. Lee; E.P. Chew; T. Roeder; P. Lendermann. Institute of Electrical and Electronics Engineers Inc., 2022. p. 760-771 (Proceedings - Winter Simulation Conference; Vol. 2022-December).
Research output: Chapter in book/report/conference proceeding › Conference contribution › Research › peer review
}
TY - GEN
T1 - A Self-Adaptive Search Space Reduction Approach for Offshore Wind Farm Installation using Multi-Installation Vessels
AU - Peng, Shengrui
AU - Szczerbicka, Helena
N1 - Funding Information: The authors are grateful for the financial support by the DFG (German Research Foundation) for the Project “OffshorePlan”, grant number LU 2049/1-1; SZ 51/33-1. We would also like to thank our colleagues, Mr. Daniel Rippel and Dr. Michael Lütjen, from BIBA for the valuable supports.
PY - 2022
Y1 - 2022
N2 - As an important part of renewable energy resources, offshore wind energy has great potential compared to its onshore counterpart despite the vast developments in recent decades. However, due to the more complex environmental condition and physical restrictions the installation of an offshore wind farm is hard to plan and predict, which often results in delays. This paper focuses on the scheduling problem in the installation phase of an offshore wind farm. We propose an adaptive search strategy based on the Apriori property and information entropy. The purpose is to prune the search space effectively and intelligently to realize an agile and swift rescheduling according to the environmental changes. For the numerical experiments, we use the environmental data obtained from the German North Sea from the year 1958 to 2007 in hourly resolution.
AB - As an important part of renewable energy resources, offshore wind energy has great potential compared to its onshore counterpart despite the vast developments in recent decades. However, due to the more complex environmental condition and physical restrictions the installation of an offshore wind farm is hard to plan and predict, which often results in delays. This paper focuses on the scheduling problem in the installation phase of an offshore wind farm. We propose an adaptive search strategy based on the Apriori property and information entropy. The purpose is to prune the search space effectively and intelligently to realize an agile and swift rescheduling according to the environmental changes. For the numerical experiments, we use the environmental data obtained from the German North Sea from the year 1958 to 2007 in hourly resolution.
UR - http://www.scopus.com/inward/record.url?scp=85147443682&partnerID=8YFLogxK
U2 - 10.1109/WSC57314.2022.10015459
DO - 10.1109/WSC57314.2022.10015459
M3 - Conference contribution
AN - SCOPUS:85147443682
SN - 978-1-6654-7662-1
T3 - Proceedings - Winter Simulation Conference
SP - 760
EP - 771
BT - Proceedings of the 2022 Winter Simulation Conference, WSC 2022
A2 - Feng, B.
A2 - Pedrielli, G.
A2 - Peng, Y.
A2 - Shashaani, S.
A2 - Song, E.
A2 - Corlu, C.G.
A2 - Lee, L.H.
A2 - Chew, E.P.
A2 - Roeder, T.
A2 - Lendermann, P.
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 2022 Winter Simulation Conference, WSC 2022
Y2 - 11 December 2022 through 14 December 2022
ER -