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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: email

(This article belongs to the 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

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.

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APA Style
Gou, H., Ning, Y. (2021). Forecasting model of photovoltaic power based on KPCA-MCS-DCNN. Computer Modeling in Engineering & Sciences, 128(2), 803-822. https://doi.org/10.32604/cmes.2021.015922
Vancouver Style
Gou H, Ning Y. Forecasting model of photovoltaic power based on KPCA-MCS-DCNN. Comput Model Eng Sci. 2021;128(2):803-822 https://doi.org/10.32604/cmes.2021.015922
IEEE Style
H. Gou and Y. Ning, "Forecasting Model of Photovoltaic Power Based on KPCA-MCS-DCNN," Comput. Model. Eng. Sci., vol. 128, no. 2, pp. 803-822. 2021. https://doi.org/10.32604/cmes.2021.015922

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cc 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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