Photovoltaic power prediction Based on Backpropagation Neural Network with Honey Badger Algorithm

As a predictive algorithm, the backpropagation (BP) neural network has been applied for the power generation anticipation of photovoltaic systems, whereas forecast accuracy in practical applications has been a problem. Therefore, to resolve the problem mentioned above, a photovoltaic (PV) power generation forecast model based on integrating a backpropagation (BP) neural network and honey badger algorithm (HBA) is proposed. Solar irradiance and ambient temperature are utilized as the input parameters to the backpropagation neural network, and the historical power generation is the output expectation. At the same time, the honey badger algorithm is introduced in the structure optimization of the network. The experiment result manifests that the optimized backpropagation neural network model outperforms the traditional backpropagation neural network model in terms of forecast accuracy and efficiency.