Huizhi Gou1,2,*, Yuncai Ning1
CMES-Computer Modeling in Engineering & Sciences, Vol.128, No.2, pp. 803-822, 2021, DOI:10.32604/cmes.2021.015922
- 22 July 2021
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
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