通过LSTM与自适应风速校准 (C-LSTM) 提高风力发电预测的准确性
Ding Wang1, Min Xu2, Zhu Guangming1
1State Grid Hunan Electric Power Company Limited Research Institute, Changsha, People's Republic of China.
本研究介绍了C-LSTM,这是一种新型模型,通过适应性校准预测的风速来改善风力发电预测. C-LSTM提高了可再生能源整合的预测准确性和可靠性.
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
- 气候变化缓解缓解 气候变化缓解
背景情况:
- 风力发电对于碳中和至关重要,需要准确的预测.
- 像LSTM这样的深度学习模型可以提前预测风力发电.
- 不准确的预测风速限制了目前风力发电预测的可靠性.
研究的目的:
- 为准确的风力发电预测开发一个先进的模型.
- 为了应对现有模型中不可靠的预测风速的挑战.
- 提高风能利用效率. 为了提高风能利用效率.
主要方法:
- 提出了一个新型模型:具有自适应风速校准 (C-LSTM) 的LSTM.
- 在训练和推断过程中,C-LSTM集成了用于自主校准风速的机制.
- 使用自适应权重参数和并发参数更新来保证历史和预测的风速.
主要成果:
- 在平均平方误差 (MSE) 和准确性方面,C-LSTM显著优于标准LSTM.
- 在25个不同的风力轮机中表现出更好的性能.
- 适应式风速校准技术有效地协调预测和实际风速之间的差异.
结论:
- C-LSTM提高了风力发电预测的准确性和可靠性.
- 适应式风速校准是改进基于深度学习的风力预测的有效策略.
- 拟议的方法有助于更有效地利用风能资源.
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