Short-term Photovoltaic Power Prediction Based on IFCM and BA-Elman

Short-term Photovoltaic Power Prediction Based on IFCM and BA-Elman

The traditional fuzzy C-means clustering algorithm (FCM) based on Euclidean distance is only applicable to clustering of spherical structures. When applied to text clustering of photovoltaic data to select similar days, it fails to take into account the difference in the importance of meteorological factors on photovoltaic power, resulting in a decrease in the accuracy and efficiency of data clustering. To solve the above problems, a short-term photovoltaic power prediction method based on improved fuzzy C-means clustering (IFCM) and bat algorithm optimization Elman neural network (BA-Elman) combination model was proposed. First, the improved fuzzy C-means clustering algorithm is used to select training samples with higher similarity to the forecast day, and then the Elman neural network prediction model optimized by bat algorithm trained by selected training samples. Finally, according to the actual data of a photovoltaic power station in Qinghai Province, a simulation experiment is carried out to verify the effectiveness of the method and model.