Photovoltaic power generation prediction method based on improved long short-term memory neural network

The invention relates to a photovoltaic power generation prediction method based on an improved long short-term memory neural network, and the method employs an EBS optimization algorithm proposed by Mohsen Shahrouzi, further proposes an OLSTM model after providing the hyper-parameters of an LSTM neural network composed of original learning data and an LSTM layer, and will obtain an optimization result with higher prediction precision and real-time performance than a conventional model. The LSTM neural network is composed of m hidden layers with m * n units and a dense layer, high-precision hyper-parameter selection can be completed by using an EBS algorithm, a hyper-parameter XGbest is input by using an OLSTM model, normalized data is trained through an LSTM training function and a prediction function in an MATLAB toolbox, then power is predicted, and finally a prediction value is obtained. Compared with a traditional LSTM model, the OLSTM based on the combination of EBS optimization and the LSTM neural netw