research-article

An Evolutionary Multiobjective Knee-Based Lower Upper Bound Estimation Method for Wind Speed Interval Forecast

Authors:

Hisao IshibuchiAuthors Info & Claims

IEEE Transactions on Evolutionary Computation, Volume 26, Issue 5

Pages 1030 - 1042

https://doi.org/10.1109/TEVC.2021.3122191

Published: 01 October 2022 Publication History

Abstract

Due to the high variability and uncertainty of the wind speed, an interval forecast can provide more information for decision makers to achieve a better energy management compared to the traditional point forecast. In this article, a knee-based lower upper bound estimation method (K-LUBE) is proposed to construct wind speed prediction intervals (PIs). First, we analyze the underlying limitations of traditional direct interval forecast methods, i.e., their obtained PIs often fail to achieve a good balance between the interval width and the coverage probability. K-LUBE resolves the difficulty based on a multiobjective optimization framework in conjunction with a knee selection criterion. Specifically, a PI-NSGA-II multiobjective optimization algorithm is designed to obtain a set of Pareto-optimal solutions. A parameter transfer and a sample training strategies are developed to significantly improve the convergence speed of the optimization procedure. Then, the knee selection criterion is introduced to select the best tradeoff solution among the obtained solutions. In comparison with traditional methods, this method can always provide a reliable PI for decision makers. The procedure is automatic and requires no parameter to be specified in advance, making it more practical for use. The effectiveness of the proposed K-LUBE method is demonstrated through extensive comparisons with four traditional direct interval forecast methods and four classical benchmark models.

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Cited By

Yamin NBhat G(2023)Uncertainty-aware Energy Harvest Prediction and Management for IoT DevicesACM Transactions on Design Automation of Electronic Systems10.1145/360637228:5(1-33)Online publication date: 29-Jun-2023
https://dl.acm.org/doi/10.1145/3606372
Zhou YRen YXu MFeng G(2023)An Improved NSGA-III Algorithm Based on Deep Q-Networks for Cloud Storage Optimization of BlockchainIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2023.324363434:5(1406-1419)Online publication date: 1-May-2023
https://dl.acm.org/doi/10.1109/TPDS.2023.3243634

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An Evolutionary Multiobjective Knee-Based Lower Upper Bound Estimation Method for Wind Speed Interval Forecast

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cover image IEEE Transactions on Evolutionary Computation

IEEE Transactions on Evolutionary Computation Volume 26, Issue 5

Oct. 2022

398 pages

ISSN:1089-778X

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1089-778X © 2021 IEEE. Personal use is permitted, but republication/redistribution requires IEEE permission. See https://www.ieee.org/publications/rights/index.html for more information.

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Published: 01 October 2022

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Cited By

Yamin NBhat G(2023)Uncertainty-aware Energy Harvest Prediction and Management for IoT DevicesACM Transactions on Design Automation of Electronic Systems10.1145/360637228:5(1-33)Online publication date: 29-Jun-2023
https://dl.acm.org/doi/10.1145/3606372
Zhou YRen YXu MFeng G(2023)An Improved NSGA-III Algorithm Based on Deep Q-Networks for Cloud Storage Optimization of BlockchainIEEE Transactions on Parallel and Distributed Systems10.1109/TPDS.2023.324363434:5(1406-1419)Online publication date: 1-May-2023
https://dl.acm.org/doi/10.1109/TPDS.2023.3243634

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