相关实验视频
优化太阳能和风能预报,使用iHow优化算法和多尺度注意力网络
Marwa Radwan1, Abdelhameed Ibrahim2, Mohamed M Abdelsalam2,3
1Faculty of Artificial Intelligence, Delta University for Science and Technology, Mansoura, 11152, Egypt. Marwa.Radwan@deltauniv.edu.eg.
Scientific reports
|March 10, 2026
概括
本研究介绍了一种混合深度学习框架,使用认知灵感算法进行可再生能源预测. 它通过优化功能选择和超参数来提高风能和太阳能预测的准确性和可扩展性.
科学领域:
- 可再生能源系统可再生能源系统
- 人工智能的人工智能是人工智能.
- 预测方法 预测方法
背景情况:
- 深度学习模型在可再生能源预测中面临着高维特征空间和超参数灵敏度的挑战.
- 这些局限性导致计算成本增加,模型通用性和稳定性降低.
研究的目的:
- 提出混合深度学习优化框架,以解决可再生能源预测中的维度和超参数灵敏性.
- 为了利用认知启发的元启发学,特别是二进制iHow优化算法 (biHOW) 和iHOW,用于特征选择和超参数调整.
主要方法:
- 利用多尺度注意网络 (MSAN) 进行时间序列预测,擅长捕获多尺度时间依赖.
- 采用biHOW进行高效的特征选择,减少模型复杂性和提高可解释性.
- 应用iHOW来微调MSAN的架构和训练参数,以优化预测性能.
主要成果:
- 混合框架在风能和太阳能发电预测方面取得了很高的准确性,最初的MSE为0.0105 (风能) 和0.0976 (太阳能).
- biHOW将平均错误分类率降至0.3925 (风力) 和0.4161 (太阳能),识别了紧的特征子集.
- iHOW进一步将小微企业降至[公式:见文本] (风力) 和[公式:见文本] (太阳能),超过了其他最先进的元启发术.
结论:
- 提出的基于iHOW的优化框架显著提高了可再生能源系统的预测准确性和计算可扩展性.
- 这种混合方法支持适应性预测,这对于智能电网中的智能能源管理至关重要.
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