使用季节性趋势分解层和ASOA优化LSTM神经网络模型进行光伏太阳能预测
Venkatachalam Mohanasundaram1, Balamurugan Rangaswamy2
1Department of Electrical and Electronics Engineering, Kongu Engineering College, Perundurai, Tamil Nadu, 638060, India. chalam203@gmail.com.
Scientific reports
|February 3, 2025
概括
本研究提出了一种新方法,用于准确的太阳能预测,通过将强大的季节趋势分解 (RSTL) 与适应性海优化算法 (ASOA) 优化的长短期记忆 (LSTM) 神经网络相结合,提高预测准确性.
科学领域:
- 可再生能源系统可再生能源系统
- 人工智能在能源中的作用
- 时间序列预测时间序列预测
背景情况:
- 全球能源需求需要像光伏 (PV) 太阳能这样的可持续解决方案.
- 精确的太阳能 (SE) 预测是具有挑战性的,因为太阳辐射 (SI) 的变化和间歇性.
- 可靠的SE预测模型对于能源管理,电网稳定性和成本降低至关重要.
研究的目的:
- 引入一种创新的方法来提高太阳能 (SE) 输出预测.
- 解决太阳辐射 (SI) 变化和间歇性在SE预测中的挑战.
- 为实际应用提高SE预测模型的准确性和可靠性.
主要方法:
- 强大的季节趋势分解 (RSTL) 的整合用于时间序列数据分析.
- 使用自适应性海优化算法 (ASOA) 优化长期短期记忆 (LSTM) 神经网络.
- 参数标准化,RSTL分解和LSTM网络概念化的细节.
主要成果:
- 与传统方法相比,提出的RSTL-ASOA-LSTM方法在预测准确度方面取得了显著的改进.
- 包括RMSE,MAE和R2在内的绩效指标表明了增强的预测能力.
- 气象因素 (MF) 的整合进一步完善了预测的准确性.
结论:
- 开发的方法通过有效处理数据分解和优化,为太阳能 (SE) 预测提供了强大的方法.
- 这些发现对未来的研究和可再生能源领域的现实应用有重大影响.
- 准确的数据分解和自适应优化是提高SE预测性能的关键.
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