An artificial immune-based hybrid multi-layer feedforward neural network for predicting grid-connected photovoltaic system output
暂无分享,去创建一个
Abstract This paper presents a Hybrid Multi-Layer Feedforward Neural Network (HMLFNN) technique for predicting the output from a grid-connected photovoltaic (GCPV) system. In the proposed HMLFNN, the Artificial Immune System (AIS) was selected as the optimizer for the training process of the Multi-Layer Feedforward Neural Network (MLFNN). AIS was used to optimize the number of neurons in the hidden layer, the learning rate, the momentum rate, the type of activation function and the learning algorithm. In addition, the MLFNN utilized solar irradiance (SI) and module temperature (MT) as its inputs and kWh energy as its output. When compared with the classically trained MLFNN, the proposed HMLFNN was found to be superior in terms of having shortest computation time and lower prediction error.
[1] Xin Yao,et al. Towards designing artificial neural networks by evolution , 1998 .
[2] Zhang Shuqing,et al. Evolving Neural Network Using Variable String Genetic Algorithm for Color Infrared Aerial Image Classification , 2008 .
[3] Ismail Musirin,et al. Partial Evolutionary ANN for Output Prediction of a Grid-Connected Photovoltaic System , 2009 .
[4] Ismail Musirin,et al. Prediction of grid-photovoltaic system output using three-variate ANN models , 2009 .