一个新的GBDT-BiLSTM混合模型,用于改进前一天光伏预测
1School of Electrical and Computer Engineering, The University of Sydney, Sydney, NSW, 2008, Australia.
Scientific reports
|September 13, 2023
概括
本研究介绍了一种新的光伏 (PV) 预测模型,使用梯度增强决策树和双向长期短期记忆网络. 该模型改善了前一天的光伏电力预测,这对电网稳定性至关重要.
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:
- 光伏发电为电网整合带来间歇性和波动性挑战.
- 现有的光伏预报方法往往依赖于不可靠的天气预报.
- 准确的光伏功率预测对于电网稳定性和运营安全至关重要.
研究的目的:
- 开发一种新的前一天光伏发电预测模型.
- 通过结合合体方法和深度学习来提高光伏功率预测的准确性和稳定性.
- 解决依赖天气预测模型的局限性.
主要方法:
- 提出了一个渐变增强决策树 (GBDT) 和双向长期短期记忆 (BiLSTM) 混合模型.
- 利用教师强迫机制来整合GBDT和BiLSTM.
- 采用Adam算法来训练BiLSTM组件,以提高时间序列预测的准确性.
- 集成的梯度提升弱学习者和决策树强学习者在GBDT.
主要成果:
- GBDT-BiLSTM 模型在光伏功率预测方面展示了有效性和稳定性.
- 该模型使用历史时间序列数据实现了准确的前一天预测.
- 实验结果验证了模型的性能与其他预测方法相比.
结论:
- 拟议的GBDT-BiLSTM模型为前一天的光伏电力预测提供了可靠的解决方案.
- 这种方法减轻了对天气预报的依赖,提高了预测稳定性.
- 该模型有助于可再生能源稳定地融入电网.
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