A hybrid model based on the photovoltaic conversion model and artificial neural network model for short-term photovoltaic power forecasting - Chen, Ran; Gao, Shaowei; Zhao, Yao; Li, Dongdong; Lin, Shunfu (Frontiers in Energy Research, 2024)
Photovoltaic (PV) power is greatly uncertain due to the random meteorological parameters. Therefore, the accurate PV power forecasting results are significant for the dispatching of power and improving of system stability. In this paper, a hybrid forecasting model is proposed for one-day ahead PV power forecasting under different cloud amount conditions. The proposed model consists of an improved artificial neural network (ANN) algorithm and a PV power conversion model. First, the ANN model is designed to forecast plane of array (POA) irradiance and ambient temperature. Backpropagation methods, Gradient descent methods, and L2 regularization methods are applied in the structure of the ANN model to achieve the best weights, improve the prediction accuracy, and alleviate the effect of the over-fitting. Second, the PV power conversion model employs the forecasted results of POA irradiance and ambient temperature to determine the PV power produced by a PV module. In addition to the basic temperature factor, an environmental efficiency and a reflection efficiency are incorporated into the conversion model to account for real PV module losses. The performance of the proposed model is validated with real weather and PV power data from Alice Spring and Climate Data Store. Results show that the model improves forecast accuracy compared to four benchmark models. Specifically, it reduces Root Mean Square Error (RMSE) and nRMSE by up to 25% under cloudy conditions and offers a 3% shorter training time compared to Extreme Gradient Boosting.