一种基于数据的模型方法,用于在电力市场中中长期预测电价
Jun Hu1, Shaotang Cai2, Feng Li3,4
1Faculty of Medical Informatics and Engineering, Hunan University of Medicine, Huaihua, 418000, China.
Scientific reports
|October 23, 2025
概括
准确的中长期电价预测得到了新的FT-GWO-CNN-LSTM-Attention算法的改进. 这种方法提高了适应性,并减少了精确能源价格预测的波动性.
科学领域:
- 能源经济学 能源经济学
- 计算智能是一种计算智能.
- 时间序列分析时间序列分析
背景情况:
- 准确的中长期电力价格预测对于市场参与者的投标策略和成本减轻至关重要.
- 现有的预测模型面临着高维数据,适应性差,价格波动等挑战.
研究的目的:
- 为准确的中长期电价预测提出数据驱动的方法.
- 提高预测模型的适应性,减少数据维度和价格波动.
主要方法:
- 使用决策树重要性评估选择了关键数据集 (历史电力,煤炭,天然气价格).
- 用快速里埃转换 (FFT) 消除了数据序列,减少了波动.
- 一个灰狼优化 (GWO) -CNN-LSTM-注意力模型被开发用于预测.
主要成果:
- 拟议的FT-GWO-CNN-LSTM-Attention (FGCLA) 算法实现了显著的预测精度改进.
- 与LSTM相比,FGCLA的平均准确度提高了57.21%,与Transformer相比,平均准确度提高了49.69%.
- 该算法有效地减少了中长期电价预测中的预测错误.
结论:
- FGCLA算法为电力价格预测提供了改进的维度减小,适应性和波动性抑制.
- 开发的模型为准确的中长期电价预测提供了强大的解决方案.
- 这种方法可以帮助市场参与者优化战略和有效管理支出.
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