A Combined Optimization Structure of Adaptive Neuro-fuzzy Inference System for Probabilistic Photovoltaic Power Forecasting

Lilin Cheng; Haixiang Zang; Tao Ding; Miaomiao Wang; Zhinong Wei; Guoqiang Sun

doi:10.1109/PESGM40551.2019.8973477

A Combined Optimization Structure of Adaptive Neuro-fuzzy Inference System for Probabilistic Photovoltaic Power Forecasting

Lilin Cheng
, Haixiang Zang
, Tao Ding
, Miaomiao Wang
, Zhinong Wei
, Guoqiang Sun

School of Electrical Engineering

Hohai University

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

4 Scopus citations

Abstract

The uncertainties of solar energy due to weather fluctuations will cause impact on photovoltaic (PV) integrated power system, limiting its further development. Hence, a novel probabilistic PV power forecasting approach based on lower upper bound estimation (LUBE) is proposed in this study, which consists of adaptive neuro-fuzzy inference system (ANFIS), modified grey wolf optimization (MGWO) algorithm and kernel density estimation (KDE). In the proposed method, two ANFIS models are combined as a group and optimized using MGWO. The output prediction intervals (PIs) from various groups are also merged together through KDE, which enhance the robustness and reliability of a sole optimized model. Owing to different quality indicators that measure diverse aspects of PIs, the proposed method achieves superior prediction results when compared with conventional forecasting methods.

Original language	English
Title of host publication	2019 IEEE Power and Energy Society General Meeting, PESGM 2019
Publisher	IEEE Computer Society
ISBN (Electronic)	9781728119816
DOIs	https://doi.org/10.1109/PESGM40551.2019.8973477
State	Published - Aug 2019
Event	2019 IEEE Power and Energy Society General Meeting, PESGM 2019 - Atlanta, United States Duration: 4 Aug 2019 → 8 Aug 2019

Publication series

Name	IEEE Power and Energy Society General Meeting
Volume	2019-August
ISSN (Print)	1944-9925
ISSN (Electronic)	1944-9933

Conference

Conference	2019 IEEE Power and Energy Society General Meeting, PESGM 2019
Country/Territory	United States
City	Atlanta
Period	4/08/19 → 8/08/19

UN SDGs

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

SDG 7 Affordable and Clean Energy

Keywords

Adaptive neuro-fuzzy inference system
lower upper bound estimation
modified grey wolf optimization
photovoltaic power
probabilistic forecasting

Access to Document

10.1109/PESGM40551.2019.8973477

Cite this

Cheng, L., Zang, H., Ding, T., Wang, M., Wei, Z., & Sun, G. (2019). A Combined Optimization Structure of Adaptive Neuro-fuzzy Inference System for Probabilistic Photovoltaic Power Forecasting. In 2019 IEEE Power and Energy Society General Meeting, PESGM 2019 Article 8973477 (IEEE Power and Energy Society General Meeting; Vol. 2019-August). IEEE Computer Society. https://doi.org/10.1109/PESGM40551.2019.8973477

@inproceedings{89ab50b69e9a4995b1155663ad2dee95,

title = "A Combined Optimization Structure of Adaptive Neuro-fuzzy Inference System for Probabilistic Photovoltaic Power Forecasting",

abstract = "The uncertainties of solar energy due to weather fluctuations will cause impact on photovoltaic (PV) integrated power system, limiting its further development. Hence, a novel probabilistic PV power forecasting approach based on lower upper bound estimation (LUBE) is proposed in this study, which consists of adaptive neuro-fuzzy inference system (ANFIS), modified grey wolf optimization (MGWO) algorithm and kernel density estimation (KDE). In the proposed method, two ANFIS models are combined as a group and optimized using MGWO. The output prediction intervals (PIs) from various groups are also merged together through KDE, which enhance the robustness and reliability of a sole optimized model. Owing to different quality indicators that measure diverse aspects of PIs, the proposed method achieves superior prediction results when compared with conventional forecasting methods.",

keywords = "Adaptive neuro-fuzzy inference system, lower upper bound estimation, modified grey wolf optimization, photovoltaic power, probabilistic forecasting",

author = "Lilin Cheng and Haixiang Zang and Tao Ding and Miaomiao Wang and Zhinong Wei and Guoqiang Sun",

note = "Publisher Copyright: {\textcopyright} 2019 IEEE.; 2019 IEEE Power and Energy Society General Meeting, PESGM 2019 ; Conference date: 04-08-2019 Through 08-08-2019",

year = "2019",

month = aug,

doi = "10.1109/PESGM40551.2019.8973477",

language = "英语",

series = "IEEE Power and Energy Society General Meeting",

publisher = "IEEE Computer Society",

booktitle = "2019 IEEE Power and Energy Society General Meeting, PESGM 2019",

}

Cheng, L, Zang, H, Ding, T, Wang, M, Wei, Z & Sun, G 2019, A Combined Optimization Structure of Adaptive Neuro-fuzzy Inference System for Probabilistic Photovoltaic Power Forecasting. in 2019 IEEE Power and Energy Society General Meeting, PESGM 2019., 8973477, IEEE Power and Energy Society General Meeting, vol. 2019-August, IEEE Computer Society, 2019 IEEE Power and Energy Society General Meeting, PESGM 2019, Atlanta, United States, 4/08/19. https://doi.org/10.1109/PESGM40551.2019.8973477

A Combined Optimization Structure of Adaptive Neuro-fuzzy Inference System for Probabilistic Photovoltaic Power Forecasting. / Cheng, Lilin; Zang, Haixiang; Ding, Tao et al.
2019 IEEE Power and Energy Society General Meeting, PESGM 2019. IEEE Computer Society, 2019. 8973477 (IEEE Power and Energy Society General Meeting; Vol. 2019-August).

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

TY - GEN

T1 - A Combined Optimization Structure of Adaptive Neuro-fuzzy Inference System for Probabilistic Photovoltaic Power Forecasting

AU - Cheng, Lilin

AU - Zang, Haixiang

AU - Ding, Tao

AU - Wang, Miaomiao

AU - Wei, Zhinong

AU - Sun, Guoqiang

PY - 2019/8

Y1 - 2019/8

N2 - The uncertainties of solar energy due to weather fluctuations will cause impact on photovoltaic (PV) integrated power system, limiting its further development. Hence, a novel probabilistic PV power forecasting approach based on lower upper bound estimation (LUBE) is proposed in this study, which consists of adaptive neuro-fuzzy inference system (ANFIS), modified grey wolf optimization (MGWO) algorithm and kernel density estimation (KDE). In the proposed method, two ANFIS models are combined as a group and optimized using MGWO. The output prediction intervals (PIs) from various groups are also merged together through KDE, which enhance the robustness and reliability of a sole optimized model. Owing to different quality indicators that measure diverse aspects of PIs, the proposed method achieves superior prediction results when compared with conventional forecasting methods.

AB - The uncertainties of solar energy due to weather fluctuations will cause impact on photovoltaic (PV) integrated power system, limiting its further development. Hence, a novel probabilistic PV power forecasting approach based on lower upper bound estimation (LUBE) is proposed in this study, which consists of adaptive neuro-fuzzy inference system (ANFIS), modified grey wolf optimization (MGWO) algorithm and kernel density estimation (KDE). In the proposed method, two ANFIS models are combined as a group and optimized using MGWO. The output prediction intervals (PIs) from various groups are also merged together through KDE, which enhance the robustness and reliability of a sole optimized model. Owing to different quality indicators that measure diverse aspects of PIs, the proposed method achieves superior prediction results when compared with conventional forecasting methods.

KW - Adaptive neuro-fuzzy inference system

KW - lower upper bound estimation

KW - modified grey wolf optimization

KW - photovoltaic power

KW - probabilistic forecasting

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DO - 10.1109/PESGM40551.2019.8973477

M3 - 会议稿件

AN - SCOPUS:85079069896

T3 - IEEE Power and Energy Society General Meeting

BT - 2019 IEEE Power and Energy Society General Meeting, PESGM 2019

PB - IEEE Computer Society

T2 - 2019 IEEE Power and Energy Society General Meeting, PESGM 2019

Y2 - 4 August 2019 through 8 August 2019

ER -

A Combined Optimization Structure of Adaptive Neuro-fuzzy Inference System for Probabilistic Photovoltaic Power Forecasting

Abstract

Publication series

Conference

UN SDGs

Keywords

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