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A multi-input and dual-output wind speed interval forecasting system based on constrained multi-objective optimization problem and model averaging
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AI
DOI：10.1016/j.enconman.2024.118909.png)
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Abstract
En 中文
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 + gamma 1 and 1 - gamma 2, 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 gamma 1 and gamma 2 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.
Keywords:
Lower and upper bound estimation
Wind speed interval forecasting
Constrained multi-objective optimization
problem
Dual-output neural network
Model averaging
Journal
IF:
10.9
Papers:
2.0W
Citations:
11.3W
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