当前位置: X-MOL 学术Appl. Energy › 论文详情
Our official English website, www.x-mol.net, welcomes your feedback! (Note: you will need to create a separate account there.)
Probabilistic solar power forecasting based on weather scenario generation
Applied Energy ( IF 11.2 ) Pub Date : 2020-03-19 , DOI: 10.1016/j.apenergy.2020.114823
Mucun Sun , Cong Feng , Jie Zhang

Probabilistic solar power forecasting plays an important role in solar power grid integration and power system operations. One of the most popular probabilistic solar forecasting methods is to feed simulated explanatory weather scenarios into a deterministic forecasting model. However, the correlation among different explanatory weather variables are seldom considered during the scenario generation process. This paper presents an improved probabilistic solar power forecasting framework based on correlated weather scenario generation. Copula is used to model a multivariate joint distribution between predicted weather variables and observed weather variables. Massive weather scenarios are obtained by deriving a conditional probability density function given a current weather prediction by using the Bayesian theory. The generated weather scenarios are used as input variables to a machine learning-based multi-model solar power forecasting model, where probabilistic solar power forecasts are obtained. The effectiveness of the proposed probabilistic solar power forecasting framework is validated by using seven solar farms from the 2000-bus synthetic grid system in Texas. Numerical results of case studies at the seven sites show that the developed probabilistic solar power forecasting methodology has improved the pinball loss metric score by up to 140% compared to benchmark models.



中文翻译:

基于天气情景生成的概率太阳能预测

概率太阳能发电预测在太阳能电网集成和电力系统运营中发挥着重要作用。最受欢迎的概率太阳预报方法之一是将模拟的解释性天气情况输入确定性的预测模型中。但是,在场景生成过程中很少考虑不同的解释性天气变量之间的相关性。本文提出了一种基于相关天气情景生成的改进的概率太阳能预测框架。Copula用于建模预测天气变量和观测天气变量之间的多元联合分布。通过使用贝叶斯理论在给定当前天气预报的情况下推导条件概率密度函数来获得大规模天气场景。生成的天气情景用作基于机器学习的多模型太阳能预测模型的输入变量,在该模型中获得概率太阳能预测。通过使用得克萨斯州2000辆公共汽车的合成电网系统中的7个太阳能发电场,验证了所提议的概率太阳能预测框架的有效性。在这七个地点进行的案例研究的数值结果表明,与基准模型相比,已开发的概率太阳能预测方法已将弹球损失指标得分提高了140%。通过使用得克萨斯州2000辆公共汽车的合成电网系统中的7个太阳能发电场,验证了所提议的概率太阳能预测框架的有效性。在这七个地点进行的案例研究的数值结果表明,与基准模型相比,已开发的概率太阳能预测方法已将弹球损失指标得分提高了140%。通过使用得克萨斯州2000辆公交车的合成电网系统中的7个太阳能发电场,验证了所提议的概率太阳能预测框架的有效性。在这七个地点进行的案例研究的数值结果表明,与基准模型相比,已开发的概率太阳能预测方法已将弹球损失指标得分提高了140%。

更新日期:2020-03-20
down
wechat
bug