【摘要】 光伏功率的准确预测对于电力系统的调度、决策至关重要。为提高光伏功率预测精度，文中基于数据驱动原理，提出基于相似日和双层校正长短时记忆（long short-term memory, LSTM）的光伏功率短期预测方法。首先，对光伏功率和相关气象数据进行归一化处理，并通过皮尔逊系数确定影响光伏功率的关键因素，降低训练数据维度。然后，利用弗雷歇距离算法匹配待预测日的相似日，提升训练数据质量。最后，根据数值天气预报，基于特征学习方法在基准层LSTM中得到光伏功率一次预测值，并基于时间序列方法在校正层LSTM中得到光伏功率误差预测值，对一次预测值进行校正，得到最终预测值。以实地采集的真实数据为例，选取晴天、多云、雨天等不同天气下的参考日作为待预测对象进行算例分析。结果表明，使用文中所提模型与方法在不同条件下均能有效预测未来24h的短期光伏功率，相比已有方法能大幅提升预测精度。更多还原

【Abstract】 Accurate forecasting of photovoltaic(PV) power is essential for power system dispatch and decision-making. To enhance the prediction accuracy of PV power, a data-driven short-term forecasting method based on similar days and a bi-layer correction long short-term memory(LSTM) model is proposed. Firstly, both PV power and related meteorological data are normalized, and key factors influencing PV power are identified using the Pearson correlation coefficient, reducing the dimensions of the training dataset. Nextly, the Fréchet distance algorithm is applied to match similar days with the target prediction day, improving the quality of the training data. Then, based on numerical weather predictions, the initial PV power forecast is obtained through the baseline LSTM using feature learning. A correction LSTM, using a time series approach,predicts the error and adjusts the initial forecast to produce the final prediction. A case study uses real-world data under different weather conditions, i.e., sunny, cloudy, and rainy conditions. It shows that the proposed model consistently delivers accurate short-term PV power predictions for the next 24 hours. The model significantly improves accuracy compared to existing methods.更多还原