Article: Simulation and visualisation for a wind power prediction model based on structural attention LSTM and environmental correction Journal: International Journal of Information and Communication Technology (IJICT) 2025 Vol.26 No.20 pp.56 - 74 Abstract: With the increasing share of renewable energy, its volatility poses challenges to grid dispatching, making wind power prediction crucial. Existing methods mainly include point forecasting and probabilistic forecasting, but the former struggles to capture fluctuations, while the latter lacks reasonable scenario generation for grid integration. Additionally, current approaches fail to fully utilise wind farm spatial structures and environmental factors, limiting prediction accuracy and generalisation. To address this, this paper proposes a scenario generation model (SLEP) based on structural attention LSTM and environmental correction. SLEP integrates temporal wind power characteristics, turbine spatial structures, and environmental factors, built upon TimeGAN. SA-LSTM combines a graph convolutional network (GCN) with LSTM to capture spatiotemporal wind power features, while the environmental correction module (ERM) employs cross-attention to embed environmental variables, improving sample adaptability. Experiments show that SLEP outperforms existing methods in accuracy, scenario diversity, and environmental adaptability, providing reliable support for grid dispatching. Inderscience Publishers - linking academia, business and industry through research

Title: Simulation and visualisation for a wind power prediction model based on structural attention LSTM and environmental correction

Authors: Yunuo Chen

Addresses: College of Electrical Engineering and New Energy, China Three Gorges University, Yichang, Hubei – 443002, China

Abstract: With the increasing share of renewable energy, its volatility poses challenges to grid dispatching, making wind power prediction crucial. Existing methods mainly include point forecasting and probabilistic forecasting, but the former struggles to capture fluctuations, while the latter lacks reasonable scenario generation for grid integration. Additionally, current approaches fail to fully utilise wind farm spatial structures and environmental factors, limiting prediction accuracy and generalisation. To address this, this paper proposes a scenario generation model (SLEP) based on structural attention LSTM and environmental correction. SLEP integrates temporal wind power characteristics, turbine spatial structures, and environmental factors, built upon TimeGAN. SA-LSTM combines a graph convolutional network (GCN) with LSTM to capture spatiotemporal wind power features, while the environmental correction module (ERM) employs cross-attention to embed environmental variables, improving sample adaptability. Experiments show that SLEP outperforms existing methods in accuracy, scenario diversity, and environmental adaptability, providing reliable support for grid dispatching.

Keywords: wind power forecasting; deep generative model; structural attention LSTM; environmental correction; scenario generation.

DOI: 10.1504/IJICT.2025.146808

International Journal of Information and Communication Technology, 2025 Vol.26 No.20, pp.56 - 74

Received: 14 Mar 2025
Accepted: 07 Apr 2025
Published online: 18 Jun 2025 *

Title: Simulation and visualisation for a wind power prediction model based on structural attention LSTM and environmental correction

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