A probabilistic approach for short-term prediction of wind gust speed using ensemble learning

Wang, Hao; Zhang, Y. M.; Mao, Jianxiao; Wan, Hua‐Ping

doi:10.1016/j.jweia.2020.104198

Cited by 69 publications

(25 citation statements)

References 50 publications

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“…Such adjustments, however, do not prevent orientation oscillations occurrence, which still may result in an instantaneously-wrong RIS configuration. Moreover, the rapid variability of the meteorological phenomena and their difficult predictability in the short-term, make real-time RIS control unpractical to overcome the UAV undesired movements effect [16].…”

Section: Introductionmentioning

confidence: 99%

RISe of Flight: RIS-Empowered UAV Communications for Robust and Reliable Air-to-Ground Networks

Mursia¹,

Devoti²,

Sciancalepore³

et al. 2021

IEEE Open J. Commun. Soc.

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Next generation mobile networks need to expand towards uncharted territories in order to enable the digital transformation of society. In this context, aerial devices such as unmanned aerial vehicles (UAVs) are expected to address this gap in hard-toreach locations. However, limited battery-life is an obstacle for the successful spread of such solutions. Reconfigurable intelligent surfaces (RISs) represent a promising solution addressing this challenge since on-board passive and lightweight controllable devices can efficiently reflect the signal propagation from the ground BSs towards specific target areas. In this paper, we focus on airto-ground networks where UAVs equipped with RIS can fly over selected areas to provide connectivity. In particular, we study how to optimally compensate flight effects and propose RiFe as well as its practical implementation Fair-RiFe that automatically configure RIS parameters accounting for undesired UAV oscillations due to adverse atmospheric conditions. Our results show that both algorithms provide robustness and reliability while outperforming state-of-the-art solutions in the multiple conditions studied.

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Section: Introductionmentioning

confidence: 99%

RISe of Flight: RIS-Empowered UAV Communications for Robust and Reliable Air-to-Ground Networks

Mursia¹,

Devoti²,

Sciancalepore³

et al. 2021

IEEE Open J. Commun. Soc.

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show abstract

“…Wang et al ensembled three kinds of machine learning approaches involving the random forest model, long short‐term memory model, and Gaussian process regression model to forecast wind gusts. The sufficient accuracy and generalization performance of the established model was then verified by a series of empirical studies 28 . Yu et al presented a novel hybrid ensemble model in forecasting monthly biofuel production.…”

Section: Introductionmentioning

confidence: 91%

“…The sufficient accuracy and generalization performance of the established model was then verified by a series of empirical studies. 28 Yu et al presented a novel hybrid ensemble model in forecasting monthly biofuel production. In this model, the original time series was decomposed and reconstructed into multi-mode by the empirical mode decomposition method and fine-to-coarse approach, after which the modes were separately predicted and the results of the individual predictions were accumulated to generate the final prediction results.…”

Section: Overview Of Studies On Carbon Price Forecasting and Other mentioning

confidence: 99%

An ensemble‐driven long short‐term memory model based on mode decomposition for carbon price forecasting of all eight carbon trading pilots in China

Sun

2020

Energy Science & Engineering

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Global climate change, which was triggered by the excessive emissions of greenhouse gases, has become an obstacle in sustainable development. Among all greenhouse gases, carbon dioxide is the most prominent and is artificially controllable. China ranks as the largest emitter of carbon dioxide worldwide and is a pioneer in economic growth. 1 As the international community is advocating in reducing carbon emissions, Chinese carbon emissions and its corresponding

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“…In recent decades, various approaches have been proposed for the prediction of wind speeds with the purpose of wind energy forecasting or disaster prevention [4,5]. The approaches can be classified into two categories, including the physical method and the statistical method [6,7].…”

Section: Introductionmentioning

confidence: 99%

Performance Evaluation of Linear and Nonlinear Models for Short-Term Forecasting of Tropical-Storm Winds

et al. 2021

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Wind-sensitive structures usually suffer from violent vibrations or severe damages under the action of tropical storms. It is of great significance to forecast tropical-storm winds in advance for the sake of reducing or avoiding consequent losses. The model used for forecasting becomes a primary concern in engineering applications. This paper presents a performance evaluation of linear and nonlinear models for the short-term forecasting of tropical storms. Five extensively employed models are adopted to forecast wind speeds using measured samples from the tropical storm Rumbia, which facilitates a comparison of the predicting performances of different models. The analytical results indicate that the autoregressive integrated moving average (ARIMA) model outperforms the other models in the one-step ahead prediction and presents the least forecasting errors in both the mean and maximum wind speeds. However, the support vector regression (SVR) model has the worst performance on the selected dataset. When it comes to the multi-step ahead forecasting, the prediction error of each model increases as the number of steps expands. Although each model shows an insufficient ability to capture the variation of future wind speed, the ARIMA model still appears to have the least forecasting errors. Hence, the ARIMA model can offer effective short-term forecasting of tropical-storm winds in both one-step and multi-step scenarios.

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A probabilistic approach for short-term prediction of wind gust speed using ensemble learning

Cited by 69 publications

References 50 publications

RISe of Flight: RIS-Empowered UAV Communications for Robust and Reliable Air-to-Ground Networks

RISe of Flight: RIS-Empowered UAV Communications for Robust and Reliable Air-to-Ground Networks

An ensemble‐driven long short‐term memory model based on mode decomposition for carbon price forecasting of all eight carbon trading pilots in China

Performance Evaluation of Linear and Nonlinear Models for Short-Term Forecasting of Tropical-Storm Winds

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