短期太阳能预测:LSTM和GRU的集成计算智能
Aneela Zameer1, Fatima Jaffar1, Farah Shahid2
1Department of Computer & Information Sciences, Pakistan Institute of Engineering and Applied Sciences, Islamabad, Pakistan.
PloS one
|October 4, 2023
概括
准确的太阳能发电场发电预测至关重要. 这项研究发现,水平太阳辐射和水和度不足显著影响太阳能光伏发电量,Bi-LSTM模型提供了卓越的预测准确性.
科学领域:
- 可再生能源系统可再生能源系统
- 机器学习应用 机器学习应用
- 气候科学 气候科学
背景情况:
- 错误的太阳能发电场发电预测带来了运营和财务挑战.
- 准确的预测对于电网稳定性,储备规划和在紧急情况下管理电力供应至关重要.
研究的目的:
- 评估气候条件对太阳能发电的影响.
- 开发和评估用于精确太阳能预测的机器学习和深度学习模型.
主要方法:
- 使用了Lasso,Ridge,ElasticNet,支持向量的回归和深度学习模型 (LSTM,Bi-LSTM,GRU).
- 使用MAE,RMSE,MSE和MAPE指标评估模型性能.
- 分析了基于阿默斯特天气数据的五分钟间隔太阳能发电.
主要成果:
- 横向太阳辐射和水和不足与太阳能光伏发电产生有很强的正相关性.
- 所有机器学习模型都表现出良好的性能.
- 通过Bi-LSTM实现了最好的预测准确度,并且错误指标最小 (MAE,RMSE,MSE,MAPE).
结论:
- 该研究验证了RNN变体的有效性,特别是Bi-LSTM,用于准确和可靠的太阳能预测.
- 结合LSTM,Bi-LSTM和GRU的混合模型显示出多步预测的前景.
- 这些研究结果支持将先进的预测模型整合到太阳能发电场管理中,以改善太阳能发电场管理.
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