Accurate photovoltaic power prediction models based on deep convolutional neural networks and gated recurrent units
N. Mohammed Sabri, Mohammed El Hassouni
Abstract
Solar energy is a feasible alternative to traditional sources of energy. However, the intermittent and random nature of photovoltaic power generation poses a challenge to the reliable and cost-effective functioning of active distribution systems. Photovoltaic power forecasting is a critical instrument in solar photovoltaic power plants for improving energy delivery quality to the grid and lowering weather-related ancillary expenses. This paper proposes a new hybrid deep learning model called convolutional neural network (CNN) and gated recurrent unit (GRU). Convolutional layers have the potential to learn complicated characteristics from raw data automatically. GRU layers, on the other hand, can learn numerous parallel sequences of input data immediately. The generated prediction model was compared to newly established deep-learning-based algorithms of CNN, GRU, recurrent neural network (RNN), and long short-term memory (LSTM) to test CNN-GRU performance. The photovoltaic power data collected in Alice Springs, Australia, is utilized. In terms of accuracy and efficacy, the values of mean absolute error (MAE), root mean squared error (RMSE), mean bias error (MBE), mean squared error (MSE) and the coefficient of determination (R2) were 0.068 kW, 0.103 kW, 0.032 kW, 0.010 kW, and 99.91%, respectively. The experimental findings show that the proposed model outperforms alternative baseline models.