An Optimized Offline Random Forests-Based Model for Ultra-Short-Term Prediction of PV Characteristics

Abstract

The fluctuation of meteorological data causes random changes in photovoltaic (PV) performance, which may negatively affect the stability and reliability of the electrical grid. This paper proposes a new ultra-short-term offline hybrid prediction model for PV I–V characteristic curves based on the dynamic characteristics of the meteorological data on a 15-min basis. The proposed hybrid prediction model is a combination of the random forests (RFs) prediction technique and the ant-lion optimizer (ALO). ALO is used to optimize the hyper-parameters of the RFs model, which aims to improve its performance in terms of accuracy and computational time. The performance of the proposed hybrid prediction model is compared with that of conventional RFs, RFs-iteration, generalized regression neural network (GRNN), GRNN-iteration, GRNN-ALO, a cascade-forward neural network (CFNN), CFNN-iteration, CFNN-ALO, feed-forward neural network (FFNN), FFNN-iteration, and FFNN-ALO models. The result shows that the I–V characteristic-curve prediction accuracy, in terms of the root-mean-squared error, mean bias error, and mean absolute percentage error of the proposed model are 0.0091 A, 0.0028 A, and 0.1392%, respectively, with an accuracy of 99.86%. Moreover, the optimization, training, and testing times are 162.15, 10.1919, and 0.1237 s, respectively. Therefore, the proposed model performs better than the aforementioned models and the other existing models in the literature. Accordingly, the proposed hybrid (RFs-ALO) offline model can significantly improve the accuracy of PV performance prediction, especially in grid-connected PV system applications.