优化太阳辐射预测:通过ADAM和Cuckoo搜索算法增强ANN模型
Muhammad Sadiq1, Muhammad Jibreel Sammar2, Muhammad Anwaar Saeed2
1Shenzhen University of Information Technology, Shenzhen, China.
PloS one
|December 5, 2025
概括
准确的太阳辐射预测对于可再生能源的整合至关重要. 这项研究引入了使用子搜索算法 (CSA) 和自适应时刻估计 (ADAM) 的混合模型,以优化人工神经网络 (ANN),显著提高预测准确性.
科学领域:
- 能源系统工程 能源系统工程
- 人工智能的人工智能
- 气象预报 气象预报
背景情况:
- 越来越多的能源需求和环境问题需要将可再生能源 (RES) 整合到电网中.
- 再生能源的间歇性质需要可靠的预测,以便有效地管理电网.
- 人工神经网络 (ANN) 在太阳辐射预测中的准确性受到随机重量初始化的限制.
研究的目的:
- 为了提高基于人工神经网络 (ANN) 的太阳辐射预测准确度.
- 建议和评估混合预测模型,使用优化算法优化ANN权重.
- 为了比较ADAM优化的ANN和CSA-ADAM优化的ANN模型的性能.
主要方法:
- 开发了两种混合太阳辐射预测模型:ADAM优化的ANN和CSA-ADAM优化的ANN.
- 采用子搜索算法 (CSA) 和自适应时刻估计 (ADAM) 来优化ANN边缘权重.
- 使用实际天气数据和标准错误指标 (MSE,MAPE,MAE,RMSE) 验证的模型.
主要成果:
- 在CSA-ADAM优化的ANN模型中,MSE=0.25,MAPE=0.17%,MAE=0.43和RMSE=0.50.的精度更高.
- 在ADAM优化的ANN模型中,MSE=0.52,MAPE=0.18%,MAE=0.64和RMSE=0.72.
- 预测时间相似,CSA-ADAM优化的ANN在0.1110 ± 0.0058秒,ADAM优化的ANN在0.1093 ± 0.0085秒.
结论:
- 使用CSA优化ANN权重显著提高了太阳辐射预测的准确性.
- CSA-ADAM混合模型为太阳能预测提供了更准确,更可靠的方法.
- 混合优化技术有效地克服了用于 RES 预测的标准 ANN 模型的局限性.
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