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Accurate prediction of short-term photovoltaic power generation via a novel double-input-rule-modules stacked deep fuzzy method

文献类型：期刊论文

作者Li, Chengdong1; Zhou, Changgeng1; Peng, Wei1; Lv, Yisheng2; Luo, Xin1
刊名ENERGY
出版日期2020-12-01
卷号212页码:13
关键词PV power generation prediction Deep fuzzy model Double input rule module Data driven method Least square method
ISSN号0360-5442
DOI10.1016/j.energy.2020.118700
通讯作者Li, Chengdong(lichengdong@sdjzu.edu.cn)
英文摘要Accurate prediction of the photovoltaic (PV) power generation is of great significance for the efficient management of the power grid. In order to strengthen the interpretability of the data-driven models for PV power prediction and to further improve the forecasting accuracy, a novel double-input-rule-modules (DIRMs) stacked deep fuzzy model (DIRM-DFM) is proposed in this study. Firstly, the proposed stacked structure of DIRM-DFM is presented. This novel modular structure adopts a bottom-up, layer-by-layer design scheme by stacking the DIRMs which has only two input variables. This scheme assures the interpretability of the proposed novel fuzzy model. Then, to guarantee the performance of DIRM-DFM, its learning mechanism, including the training data generation, the construction of the DIRMs, are given in detail. This learning mechanism has fast learning speed and excellent approximation ability, because each DIRM is optimized by the popular least square method. Finally, two real-world experiments for predicting the PV power generation are conducted to verify the proposed DIRM-DFM, and detailed comparisons are made with traditional and deep fuzzy models, shallow and deep neural networks. Experimental results clearly demonstrated that the proposed DIRM-DFM has the best accuracy and the reactively fast training speed while having the apparent advantages of interpretability. (c) 2020 Elsevier Ltd. All rights reserved.
WOS关键词ARTIFICIAL NEURAL-NETWORKS ; WEATHER FORECASTS ; OUTPUT ; MODEL ; STABILIZATION ; OPTIMIZATION ; ALGORITHM ; MACHINE ; PLANT
资助项目Taishan Scholar Project of Shandong Province[TSQN201812092] ; National Natural Science Foundation of China[61903226] ; Key Research and Development Program of Shandong Province[2019GGX101072] ; Key Research and Development Program of Shandong Province[2019JZZY010115] ; Youth Innovation Technology Project of Higher School in Shandong Province[2019KJN005]
WOS研究方向Thermodynamics ; Energy & Fuels
语种英语
WOS记录号WOS:000596123000001
出版者PERGAMON-ELSEVIER SCIENCE LTD
资助机构Taishan Scholar Project of Shandong Province ; National Natural Science Foundation of China ; Key Research and Development Program of Shandong Province ; Youth Innovation Technology Project of Higher School in Shandong Province
源URL[http://ir.ia.ac.cn/handle/173211/42758]  
专题自动化研究所_复杂系统管理与控制国家重点实验室_先进控制与自动化团队
通讯作者Li, Chengdong
作者单位1.Shandong Jianzhu Univ, Sch Informat & Elect Engn, Shandong Key Lab Intelligent Bldg Technol, Jinan 250101, Peoples R China
2.Chinese Acad Sci, Inst Automat, Beijing 100190, Peoples R China
推荐引用方式
GB/T 7714		 Li, Chengdong,Zhou, Changgeng,Peng, Wei,et al. Accurate prediction of short-term photovoltaic power generation via a novel double-input-rule-modules stacked deep fuzzy method[J]. ENERGY,2020,212:13.
APA Li, Chengdong,Zhou, Changgeng,Peng, Wei,Lv, Yisheng,&Luo, Xin.(2020).Accurate prediction of short-term photovoltaic power generation via a novel double-input-rule-modules stacked deep fuzzy method.ENERGY,212,13.
MLA Li, Chengdong,et al."Accurate prediction of short-term photovoltaic power generation via a novel double-input-rule-modules stacked deep fuzzy method".ENERGY 212(2020):13.

入库方式： OAI收割

来源：自动化研究所

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