对比3类LSTM功率预测方法的误差以评价业务气象预报在光伏功率预测中的作用,及训练集、测试集的不同划分对预测精度的影响。这3类功率预测方法分别是：只使用光伏功率、使用光伏功率及气象观测、使用光伏功率及气象预报。气象预报因子使用了与光伏功率相关性最高的总辐照度。分析时间段为2020年1月1日—6月30日,气象预报来自于ECMWF和NOAA/NCEP。结果表明,对于长度有限的资料,训练集、测试集的不同划分对预测模型精度会产生一定的影响。如果可使用总辐照度的观测,则短期功率预测的相对误差可降低约2.3%。与只使用光伏功率相比,既使用光伏功率又使用气象预报,短期功率预测相对误差降低约2.1%。与NOAA/NCEP气象预报相比,ECMWF气象预报明显降低了功率预测的误差。相比于只使用光伏功率,增加气象预报可提高预测精度。
Abstract
The errors of three types of LSTM power prediction methods were compared to evaluate the role of operational weather forecast in photovoltaic power prediction and the influence of different divisions of training and test sets on prediction accuracy. The three types of power prediction methods are： using photovoltaic power only, using photovoltaic power and meteorological observation, and using photovoltaic power and meteorological forecast. The meteorological variable used is total irradiance, which has the highest correlation coefficient with photovoltaic power. The analysis period is from January 1 to June 30, 2020, with weather forecasts from the ECMWF and NOAA/NCEP. The results show that, for the data with limited time length, the different division of the training and test sets will have influence on the accuracy of the prediction model. If total irradiance observation is used, the relative error of short-term power predictions can be reduced by about 2.3%. Compared with only using photovoltaic power, the relative error of short-term power prediction is reduced by about 2.1% by using both photovoltaic power and meteorological forecast. Compared with NOAA/NCEP weather forecast, ECMWF weather forecast significantly reduces the error of power prediction. Compared with only using photovoltaic power, using meteorological forecast data can enhance the accuracy of photovoltaic power prediction.
关键词
长短期记忆网络 /
光伏发电 /
功率预测 /
辐照度
Key words
long short-term memory（LSTM） /
photovoltaic power generation /
power prediction /
irradiance
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基金
大学生创新创业训练计划（XJDC202210300134）
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