融合深度学习与环境决策系统，提升可再生能源需求预测精度。
LTPNet Integration of Deep Learning and Environmental Decision Support Systems for Renewable Energy Demand Forecasting
- 结合LSTM、Transformer与粒子群优化算法，优化模型参数。
- 预测误差降低30%~35%，显著优于传统方法。
- 适合能源规划与环保政策制定者参考使用。
在全球环境变化日益严峻的背景下，准确预测并满足可再生能源需求已成为可持续业务发展的关键挑战。传统能源需求预测方法常面临数据处理复杂和预测精度低的问题。本文提出一种新方法，将深度学习技术与环境决策支持系统相结合。模型融合了LSTM、Transformer等先进深度学习技术，并采用粒子群优化（PSO）算法进行参数优化，显著提升了预测性能与实际应用价值。实验结果表明，该模型在各项指标上均实现显著改进：平均绝对误差（MAE）降低30%，平均绝对百分比误差（MAPE）下降20%，均方根误差（RMSE）减少25%，均方误差（MSE）降低35%。这些结果验证了模型在可再生能源需求预测中的有效性与可靠性。本研究为深度学习在环境决策支持系统中的应用提供了重要参考。
原文摘要 · Abstract (English)
Against the backdrop of increasingly severe global environmental changes, accurately predicting and meeting renewable energy demands has become a key challenge for sustainable business development. Traditional energy demand forecasting methods often struggle with complex data processing and low prediction accuracy. To address these issues, this paper introduces a novel approach that combines deep learning techniques with environmental decision support systems. The model integrates advanced deep learning techniques, including LSTM and Transformer, and PSO algorithm for parameter optimization, significantly enhancing predictive performance and practical applicability. Results show that our model achieves substantial improvements across various metrics, including a 30% reduction in MAE, a 20% decrease in MAPE, a 25% drop in RMSE, and a 35% decline in MSE. These results validate the model's effectiveness and reliability in renewable energy demand forecasting. This research provides valuable insights for applying deep learning in environmental decision support systems.
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