TRNet: A trend and residual network utilizing novel hilly attention mechanism for wind speed prediction in complex scenario

Shi, Peiming; Lin, Shengmao; Song, Dongran; Xu, Xuefang; Wu, Jie

doi:10.1016/j.energy.2024.133103

Energy

2024

DOI: 10.1016/j.energy.2024.133103

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TRNet: A trend and residual network utilizing novel hilly attention mechanism for wind speed prediction in complex scenario

Peiming Shi,

Shengmao Lin,

Dongran Song

et al.

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Cited by 2 publications

References 28 publications

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Decomposition based deep projection-encoding echo state network for multi-scale and multi-step wind speed prediction

Li,

Guo,

2025

Expert Systems with Applications

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Decomposition based deep projection-encoding echo state network for multi-scale and multi-step wind speed prediction

Li,

Guo,

2025

Expert Systems with Applications

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A Numerical Investigation of the Influence of the Wake for Mixed Layout Wind Turbines in Wind Farms Using FLORIS

Tian,

Wei,

Zhao

et al. 2024

JMSE

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A common retrofitting method for wind farms is the replacement of low-power turbines with high-power ones. The determination of the optimal replacement sequence for the purpose of maximizing revenue is a significant challenge. This paper employs a combination of FLORIS and a sequencing algorithm to simulate the power output resulting from the replacement of 1.5 MW small turbines with 5 MW large turbines. This study demonstrates that the optimal strategy for maximizing the overall power output is to replace the turbines in the first column. When the turbines situated in the first column have already undergone replacement or are unable to be replaced due to the characteristics of the terrain, it would be prudent to prioritize those in the final column. In the case of staggered arrangements, priority should be given to diagonal points that do not have turbines situated behind them. In the case of replacing the same number of large wind turbines, the preferred replacement option has a minimal impact on the power output of the existing small wind turbines, with an estimated reduction of 0.67%. This effectively enhances the economic efficiency of wind farm renovation.

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TRNet: A trend and residual network utilizing novel hilly attention mechanism for wind speed prediction in complex scenario

Cited by 2 publications

References 28 publications

Decomposition based deep projection-encoding echo state network for multi-scale and multi-step wind speed prediction

Decomposition based deep projection-encoding echo state network for multi-scale and multi-step wind speed prediction

A Numerical Investigation of the Influence of the Wake for Mixed Layout Wind Turbines in Wind Farms Using FLORIS

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