An Improved Mixture Density Network via Wasserstein Distance Based Adversarial Learning for Probabilistic Wind Speed Predictions

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Original languageEnglish
Pages (from-to)755-766
Number of pages12
Journal / PublicationIEEE Transactions on Sustainable Energy
Issue number2
Online published30 Nov 2021
Publication statusPublished - Apr 2022


This paper develops a novel improved mixture density network via Wasserstein distance-based adversarial learning (WA-IMDN) for achieving more accurate probabilistic wind speed predictions (PWSP). The proposed method utilizes historical supervisory control and data acquisition (SCADA) system data collected from multiple wind turbines (WTs) in different wind farms to predict the wind speed probability density function (PDF) of a targeted WT at the next timestamp. To better capture the fluctuation tendency of historical wind speed sequences and estimate parameters of the probability mixture model for approximating the wind speed PDF, an improved mixture density network (IMDN) is proposed. To address drawbacks of the traditional maximum likelihood estimation (MLE) on training the mixture density network, a Wasserstein distance (WD) based adversarial learning is developed and the reparameterization trick is employed for the gradient delivery. The effectiveness of the proposed WA-IMDN is validated based on SCADA data (One dataset is publicly accessible) by benchmarking against a set of the commonly considered and recently reported PWSP methods, such as the mixture density network (MDN), maximum likelihood estimation (MLE) based mixture density attention network (MLE-IMDN), recent DMDNN
and Improved Deep Mixture Density Network (IDMDN). Results demonstrate the superior performance of the proposed WA-IMDN on the PWSP. To demonstrate the repeatability of the presented research, we release our code at

Research Area(s)

  • Artificial neural networks, Maximum likelihood estimation, Predictive models, Probabilistic logic, Probability density function, Training, Wind speed, probabilistic prediction, neural networks, data-driven model, deep learning

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