To achieve effective intraday dispatch of photovoltaic (PV) power generation systems, a reliable ultra-short-term power generation forecasting model is required. Based on a gradient boosting strategy and a dendritic network, this paper proposes a novel ensemble prediction model, named gradient boosting dendritic network (GBDD) model which can reduce the forecast error by learning the relationship between forecast residuals and meteorological factors during the training of sub-models by means of a greedy function approximation. Unlike other machine learning models, the GBDD proposed is able to make fuller use of all meteorological factor data and has a good model interpretation. In addition, based on the structure of GBDD, this paper proposes a strategy that can improve the prediction performance of other types of prediction models. The GBDD is trained by analyzing the relationship between prediction errors and meteorological factors for compensating the prediction results of other prediction models. The experimental results show that the GBDD proposed has the benefit of achieving a higher PV power prediction accuracy for PV power generation and can be used to improve the prediction performance of other prediction models.

This research provides a comprehensive analysis of deleterious mutations in pear (Pyrus spp.) during domestication, shedding light on their impact on agronomic traits. 

This research explores 25 years of curated genetic data to identify critical genomic regions linked to key traits in Prunus crops such as peaches, almonds, apricots, and sweet cherries.