向风速和太阳辐射预测的自动化模型选择.
Konstantinos Blazakis1,2, Nikolaos Schetakis3, Paolo Bonfini4,5
1School of Electrical and Computer Engineering, Technical University of Crete, 73100 Chania, Greece.
对太阳能和风能等可再生能源 (RES) 的准确预测对电网稳定至关重要. 这项研究发现,特定的时间步骤显著影响中期风速和太阳辐射预测.
科学领域:
- 能源科学 能源科学
- 电气工程 电气工程
- 环境科学 环境科学
背景情况:
- 越来越多的电力需求需要整合可再生能源 (RES),主要是太阳能和风能.
- 太阳辐射和风速的变化给稳定的可再生能源融入电网带来了挑战.
- 准确的预测对于高效运营,成本效益和电网可靠性至关重要.
研究的目的:
- 评估各种模型,以预测风速和太阳辐射的中期 (24小时前) 预测.
- 分析预处理步骤和不同预测算法 (经典和深度学习) 的影响.
- 识别影响预测准确性的关键特征 (时间步骤).
主要方法:
- 利用了来自希腊克里特岛的实时测量数据.
- 在时间序列数据上应用了多种不同的预处理技术.
- 对比了经典和深度学习预测模型的性能.
- 研究了特定时间步骤对预测准确性的影响.
主要成果:
- 确定了特定案例研究中最准确的预测模型.
- 证明某些时间步骤对预测准确性有重大影响.
- 强调了与时间步骤相关的特征工程的重要性.
结论:
- 通过适当的模型选择和特征工程,可以实现对风速和太阳辐射的准确中期预测.
- 时间步骤的选择是改善可再生能源预测绩效的关键因素.
- 对于类似的可再生能源预测研究,建议进行广泛的模型和特征搜索.
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