A multi-input and dual-output wind speed interval forecasting system based on constrained multi-objective optimization problem and model averaging

[Display omitted] •A multi-input dual-output neural network is constructed to obtain the best interval.•Two constrained bi-objective optimization problems are established and solved.•An algorithm specifically solving constrained multi-objective problem is employed.•Coverage constraint is introduced...

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Veröffentlicht in:	Energy conversion and management Jg. 319; S. 118909
Hauptverfasser:	Lv, Mengzheng, Wang, Jianzhou, Wang, Shuai, Zhao, Yang, Gao, Jialu, Wang, Kang
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Elsevier Ltd 01.11.2024
Schlagworte:	Constrained multi-objective optimization problem Dual-output neural network Lower and upper bound estimation Model averaging Wind speed interval forecasting Dual-output neural network Constrained multi-objective optimization problem Lower and upper bound estimation Wind speed interval forecasting Model averaging
ISSN:	0196-8904
Online-Zugang:	Volltext
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Zusammenfassung:	[Display omitted] •A multi-input dual-output neural network is constructed to obtain the best interval.•Two constrained bi-objective optimization problems are established and solved.•An algorithm specifically solving constrained multi-objective problem is employed.•Coverage constraint is introduced to optimize the two optimal interval coefficients.•Model averaging is used to combine advantages to skillfully solve “no free lunch” The uncertainty analysis of wind speed forecasting using the Lower Upper Bound Estimation (LUBE) is an advanced interval prediction method that does not require assumptions about data distribution. However, previous studies have primarily relied on single neural network models, overlooking the benefits of model averaging. Moreover, they assumed symmetric upper and lower bounds of true values in training data, which may not hold for real data with asymmetric features. To address these issues, we propose a multi-input dual-output wind speed interval forecasting system (MDWSIFS). Utilizing neural network models, we create two different outputs for each model by scaling the output values with interval scaling coefficients 1 + γ1 and 1 - γ2, respectively. Subsequently, we propose two constrained multi-objective optimization problems and introduce non-dominated sorting genetic algorithm II (NSGA-II), a method that has been proven to be highly suitable for solving constrained bi-objective optimization problems. By using NSGA-II to optimize a multi-objective problem with coverage probability constraints, the optimal coefficients γ1 and γ2 are determined, thereby the prediction interval is defined. Finally, through a model averaging strategy integrated with several neural network models, we use NSGA-II to optimize the weights of sub-models to achieve a more accurate final prediction interval. The test results indicate the superiority of MDWSIFS over existing models, with the metric reaching unprecedented levels across multiple datasets. These findings not only signify an advancement in wind speed forecasting but also promise improved efficiency in wind energy utilization and reduced operational costs for power systems.
ISSN:	0196-8904
DOI:	10.1016/j.enconman.2024.118909