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A data-driven deep sequence-to-sequence long-short memory method along with a gated recurrent neural network for wind power forecasting

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  • Ahmad, Tanveer
  • Zhang, Dongdong

Abstract

Large amounts of wind power generation have an impact not only on energy markets but also on wholesale and retail market designs. Simultaneously, technological issues arise as a result of the need to ensure the smooth operation of the power grid. In the long run, high-quality wind data series are required to generate model results that lead to robust policy advice. Several techniques are commonly used to forecast short-term, nonstationary, and nonlinear wind speeds. These techniques are lacking in model optimization and data processing abilities. This lack of expertise is posing a significant challenge for reliable and stable wind power forecasting. Furthermore, the medium and long-term forecasting criteria for models (e.g., robustness, accuracy, speed) are higher, making it difficult to obtain reliable results. This study used the extended deep sequence-to-sequence long short-term memory regression (STSR-LSTM) model for time-series wind power forecasting to overcome these challenges for accurate forecasting decisions. The statistical-learning technique is used to improve the dependability of the derived features as well as the expected performance. Three different locations/sites were used and analyzed (e.g., Belgian, DSO-Connected, Elia), three different forecasting classes (e.g., week-ahead forecast, day-ahead forecast, and most recent forecast), three different seasons (e.g., monthly, seasonal, and annual), and three experimental setups. The performance of Deep STSR-LSTM is validated using two existing models and four performance evaluation indexes. Despite the fact that the input wind power load curve had a lot of variation, we were able to achieve higher forecast accuracy. Using different sites and classes, similar forecast accuracy was achieved for each season (e.g., monthly, seasonal, and annual).

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  • Ahmad, Tanveer & Zhang, Dongdong, 2022. "A data-driven deep sequence-to-sequence long-short memory method along with a gated recurrent neural network for wind power forecasting," Energy, Elsevier, vol. 239(PB).
  • Handle: RePEc:eee:energy:v:239:y:2022:i:pb:s0360544221023574
    DOI: 10.1016/j.energy.2021.122109
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    References listed on IDEAS

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