Knowledge Resource Center for Ecological Environment in Arid Area
DOI | 10.1080/15435075.2021.1904945 |
Tree-based ensemble methods for predicting the module temperature of a grid-tied photovoltaic system in the desert | |
Ziane, Abderrezzaq; Dabou, Rachid; Necaibia, Ammar; Sahouane, Nordine; Mostefaoui, Mohammed; Bouraiou, Ahmed; Khelifi, Seyfallah; Rouabhia, Abdelkrim; Blal, Mohamed | |
通讯作者 | Ziane, A (corresponding author), Route Reggane,BP 478, Adrar, Algeria. |
来源期刊 | INTERNATIONAL JOURNAL OF GREEN ENERGY |
ISSN | 1543-5075 |
EISSN | 1543-5083 |
出版年 | 2021 |
英文摘要 | The PV module temperature is a crucial parameter in the performance of a grid-tied PV station and it has an important effect on the PV system efficiency. In this work, we are interested in predicting the module temperature of a grid-tied photovoltaic system using tree-based ensemble methods, namely random forest and boosted decision tree. The linear least square method and the artificial neural network method were used as a frame of reference to evaluate the results of tree-based ensemble methods. The hyper-tuning of the tree ensemble method was done to optimize the model's parameters and to improve accuracy and prevent overfitting. All developed models have similar accuracy during the training and they are equally applicable for predicting PV module temperature. The results showed that during testing, the tree-based ensemble methods maintained their accuracy with R2 above 0.98. Meanwhile, the accuracy of other methods declined, which proves the utility of the tree-based ensemble over the classical method especially the ANN |
英文关键词 | Photovoltaic module temperature machine learning prediction |
类型 | Article ; Early Access |
语种 | 英语 |
收录类别 | SCI-E |
WOS记录号 | WOS:000637246900001 |
WOS类目 | Thermodynamics ; Green & Sustainable Science & Technology ; Energy & Fuels |
WOS研究方向 | Thermodynamics ; Science & Technology - Other Topics ; Energy & Fuels |
资源类型 | 期刊论文 |
条目标识符 | http://119.78.100.177/qdio/handle/2XILL650/352270 |
作者单位 | [Ziane, Abderrezzaq; Dabou, Rachid; Necaibia, Ammar; Sahouane, Nordine; Bouraiou, Ahmed; Khelifi, Seyfallah; Rouabhia, Abdelkrim; Blal, Mohamed] CDER, Ctr Dev Energies Renouvelables, URERMS, Unite Rech Energie Renouvelables Milieu Saharien, Adrar 01000, Algeria; [Mostefaoui, Mohammed] Ecole Natl Super Informat, Sidi Bel Abbes, Algeria |
推荐引用方式 GB/T 7714 | Ziane, Abderrezzaq,Dabou, Rachid,Necaibia, Ammar,et al. Tree-based ensemble methods for predicting the module temperature of a grid-tied photovoltaic system in the desert[J],2021. |
APA | Ziane, Abderrezzaq.,Dabou, Rachid.,Necaibia, Ammar.,Sahouane, Nordine.,Mostefaoui, Mohammed.,...&Blal, Mohamed.(2021).Tree-based ensemble methods for predicting the module temperature of a grid-tied photovoltaic system in the desert.INTERNATIONAL JOURNAL OF GREEN ENERGY. |
MLA | Ziane, Abderrezzaq,et al."Tree-based ensemble methods for predicting the module temperature of a grid-tied photovoltaic system in the desert".INTERNATIONAL JOURNAL OF GREEN ENERGY (2021). |
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