@Article{cmes.2021.015922,
AUTHOR = {Huizhi Gou, Yuncai Ning},
TITLE = {Forecasting Model of Photovoltaic Power Based on KPCA-MCS-DCNN},
JOURNAL = {Computer Modeling in Engineering \& Sciences},
VOLUME = {128},
YEAR = {2021},
NUMBER = {2},
PAGES = {803--822},
URL = {http://www.techscience.com/CMES/v128n2/43919},
ISSN = {1526-1506},
ABSTRACT = {Accurate photovoltaic (PV) power prediction can effectively help the power sector to make rational energy planning
and dispatching decisions, promote PV consumption, make full use of renewable energy and alleviate energy
problems. To address this research objective, this paper proposes a prediction model based on kernel principal
component analysis (KPCA), modified cuckoo search algorithm (MCS) and deep convolutional neural networks
(DCNN). Firstly, KPCA is utilized to reduce the dimension of the feature, which aims to reduce the redundant
input vectors. Then using MCS to optimize the parameters of DCNN. Finally, the photovoltaic power forecasting
method of KPCA-MCS-DCNN is established. In order to verify the prediction performance of the proposed model,
this paper selects a photovoltaic power station in China for example analysis. The results show that the new hybrid
KPCA-MCS-DCNN model has higher prediction accuracy and better robustness.},
DOI = {10.32604/cmes.2021.015922}
}