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Forecasting Model of Photovoltaic Power Based on KPCA-MCS-DCNN

Huizhi Gou1,2,*, Yuncai Ning1
1 School of Management, China University of Mining and Technology-Beijing, Beijing, 100083, China
2 China Energy Investment Corporation, Beijing, 100011, China
* Corresponding Author: Huizhi Gou. Email:
(This article belongs to this Special Issue: Hybrid Intelligent Methods for Forecasting in Resources and Energy Field)

Computer Modeling in Engineering & Sciences 2021, 128(2), 803-822. https://doi.org/10.32604/cmes.2021.015922

Received 24 January 2021; Accepted 21 April 2021; Issue published 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 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.

Keywords

Photovoltaic power prediction; kernel principal component analysis; modified cuckoo search algorithm; deep convolutional neural networks

Cite This Article

Gou, H., Ning, Y. (2021). Forecasting Model of Photovoltaic Power Based on KPCA-MCS-DCNN. CMES-Computer Modeling in Engineering & Sciences, 128(2), 803–822.

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This work is licensed under a Creative Commons Attribution 4.0 International License , which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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