Photovoltaic power prediction Based on Backpropagation Neural Network with Honey Badger Algorithm

Yingxue Chen; Guanxiang Feng; Linfeng Gou; Huatao Chen

doi:10.1109/CCDC58219.2023.10327009

Photovoltaic power prediction Based on Backpropagation Neural Network with Honey Badger Algorithm

Yingxue Chen
, Guanxiang Feng
, Linfeng Gou
, Huatao Chen

School of Power and Energy

Northwestern Polytechnical University Xian

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

2 Scopus citations

Abstract

As a predictive algorithm, the backpropagation (BP) neural network has been applied for the power generation anticipation of photovoltaic systems, whereas forecast accuracy in practical applications has been a problem. Therefore, to resolve the problem mentioned above, a photovoltaic (PV) power generation forecast model based on integrating a backpropagation (BP) neural network and honey badger algorithm (HBA) is proposed. Solar irradiance and ambient temperature are utilized as the input parameters to the backpropagation neural network, and the historical power generation is the output expectation. At the same time, the honey badger algorithm is introduced in the structure optimization of the network. The experiment result manifests that the optimized backpropagation neural network model outperforms the traditional backpropagation neural network model in terms of forecast accuracy and efficiency.

Original language	English
Title of host publication	Proceedings of the 35th Chinese Control and Decision Conference, CCDC 2023
Publisher	Institute of Electrical and Electronics Engineers Inc.
Pages	1922-1927
Number of pages	6
ISBN (Electronic)	9798350334722
DOIs	https://doi.org/10.1109/CCDC58219.2023.10327009
State	Published - 2023
Event	35th Chinese Control and Decision Conference, CCDC 2023 - Yichang, China Duration: 20 May 2023 → 22 May 2023

Publication series

Name	Proceedings of the 35th Chinese Control and Decision Conference, CCDC 2023

Conference

Conference	35th Chinese Control and Decision Conference, CCDC 2023
Country/Territory	China
City	Yichang
Period	20/05/23 → 22/05/23

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Keywords

Backpropagation neural network
Honey badger algorithm
Optimization
Photovoltaic

Access to Document

10.1109/CCDC58219.2023.10327009

Cite this

Chen, Y., Feng, G., Gou, L., & Chen, H. (2023). Photovoltaic power prediction Based on Backpropagation Neural Network with Honey Badger Algorithm. In Proceedings of the 35th Chinese Control and Decision Conference, CCDC 2023 (pp. 1922-1927). (Proceedings of the 35th Chinese Control and Decision Conference, CCDC 2023). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/CCDC58219.2023.10327009

Chen, Yingxue ; Feng, Guanxiang ; Gou, Linfeng et al. / Photovoltaic power prediction Based on Backpropagation Neural Network with Honey Badger Algorithm. Proceedings of the 35th Chinese Control and Decision Conference, CCDC 2023. Institute of Electrical and Electronics Engineers Inc., 2023. pp. 1922-1927 (Proceedings of the 35th Chinese Control and Decision Conference, CCDC 2023).

@inproceedings{38b0e2cdc32c4d8683905febd02fbf06,

title = "Photovoltaic power prediction Based on Backpropagation Neural Network with Honey Badger Algorithm",

abstract = "As a predictive algorithm, the backpropagation (BP) neural network has been applied for the power generation anticipation of photovoltaic systems, whereas forecast accuracy in practical applications has been a problem. Therefore, to resolve the problem mentioned above, a photovoltaic (PV) power generation forecast model based on integrating a backpropagation (BP) neural network and honey badger algorithm (HBA) is proposed. Solar irradiance and ambient temperature are utilized as the input parameters to the backpropagation neural network, and the historical power generation is the output expectation. At the same time, the honey badger algorithm is introduced in the structure optimization of the network. The experiment result manifests that the optimized backpropagation neural network model outperforms the traditional backpropagation neural network model in terms of forecast accuracy and efficiency.",

keywords = "Backpropagation neural network, Honey badger algorithm, Optimization, Photovoltaic",

author = "Yingxue Chen and Guanxiang Feng and Linfeng Gou and Huatao Chen",

note = "Publisher Copyright: {\textcopyright} 2023 IEEE.; 35th Chinese Control and Decision Conference, CCDC 2023 ; Conference date: 20-05-2023 Through 22-05-2023",

year = "2023",

doi = "10.1109/CCDC58219.2023.10327009",

language = "英语",

series = "Proceedings of the 35th Chinese Control and Decision Conference, CCDC 2023",

publisher = "Institute of Electrical and Electronics Engineers Inc.",

pages = "1922--1927",

booktitle = "Proceedings of the 35th Chinese Control and Decision Conference, CCDC 2023",

}

Chen, Y, Feng, G, Gou, L & Chen, H 2023, Photovoltaic power prediction Based on Backpropagation Neural Network with Honey Badger Algorithm. in Proceedings of the 35th Chinese Control and Decision Conference, CCDC 2023. Proceedings of the 35th Chinese Control and Decision Conference, CCDC 2023, Institute of Electrical and Electronics Engineers Inc., pp. 1922-1927, 35th Chinese Control and Decision Conference, CCDC 2023, Yichang, China, 20/05/23. https://doi.org/10.1109/CCDC58219.2023.10327009

Photovoltaic power prediction Based on Backpropagation Neural Network with Honey Badger Algorithm. / Chen, Yingxue; Feng, Guanxiang; Gou, Linfeng et al.
Proceedings of the 35th Chinese Control and Decision Conference, CCDC 2023. Institute of Electrical and Electronics Engineers Inc., 2023. p. 1922-1927 (Proceedings of the 35th Chinese Control and Decision Conference, CCDC 2023).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Photovoltaic power prediction Based on Backpropagation Neural Network with Honey Badger Algorithm

AU - Chen, Yingxue

AU - Feng, Guanxiang

AU - Gou, Linfeng

AU - Chen, Huatao

PY - 2023

Y1 - 2023

N2 - As a predictive algorithm, the backpropagation (BP) neural network has been applied for the power generation anticipation of photovoltaic systems, whereas forecast accuracy in practical applications has been a problem. Therefore, to resolve the problem mentioned above, a photovoltaic (PV) power generation forecast model based on integrating a backpropagation (BP) neural network and honey badger algorithm (HBA) is proposed. Solar irradiance and ambient temperature are utilized as the input parameters to the backpropagation neural network, and the historical power generation is the output expectation. At the same time, the honey badger algorithm is introduced in the structure optimization of the network. The experiment result manifests that the optimized backpropagation neural network model outperforms the traditional backpropagation neural network model in terms of forecast accuracy and efficiency.

AB - As a predictive algorithm, the backpropagation (BP) neural network has been applied for the power generation anticipation of photovoltaic systems, whereas forecast accuracy in practical applications has been a problem. Therefore, to resolve the problem mentioned above, a photovoltaic (PV) power generation forecast model based on integrating a backpropagation (BP) neural network and honey badger algorithm (HBA) is proposed. Solar irradiance and ambient temperature are utilized as the input parameters to the backpropagation neural network, and the historical power generation is the output expectation. At the same time, the honey badger algorithm is introduced in the structure optimization of the network. The experiment result manifests that the optimized backpropagation neural network model outperforms the traditional backpropagation neural network model in terms of forecast accuracy and efficiency.

KW - Backpropagation neural network

KW - Honey badger algorithm

KW - Optimization

KW - Photovoltaic

UR - https://www.scopus.com/pages/publications/85181800134

U2 - 10.1109/CCDC58219.2023.10327009

DO - 10.1109/CCDC58219.2023.10327009

M3 - 会议稿件

AN - SCOPUS:85181800134

T3 - Proceedings of the 35th Chinese Control and Decision Conference, CCDC 2023

SP - 1922

EP - 1927

BT - Proceedings of the 35th Chinese Control and Decision Conference, CCDC 2023

PB - Institute of Electrical and Electronics Engineers Inc.

T2 - 35th Chinese Control and Decision Conference, CCDC 2023

Y2 - 20 May 2023 through 22 May 2023

ER -

Chen Y, Feng G, Gou L, Chen H. Photovoltaic power prediction Based on Backpropagation Neural Network with Honey Badger Algorithm. In Proceedings of the 35th Chinese Control and Decision Conference, CCDC 2023. Institute of Electrical and Electronics Engineers Inc. 2023. p. 1922-1927. (Proceedings of the 35th Chinese Control and Decision Conference, CCDC 2023). doi: 10.1109/CCDC58219.2023.10327009

Photovoltaic power prediction Based on Backpropagation Neural Network with Honey Badger Algorithm

Abstract

Publication series

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UN SDGs

Keywords

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