{"title":"基于集合模型的短期光伏发电预测新方法","authors":"Yunxiu Zhang, Bingxian Li, Zhiyin Han","doi":"10.1063/5.0226761","DOIUrl":null,"url":null,"abstract":"Photovoltaic (PV) power generation is influenced by various factors, including weather conditions, the quality of PV inverters, and the cleanliness of PV modules, with weather conditions having a particularly significant impact on power output. This paper proposes a novel method for PV power generation prediction based on an ensemble forecasting model, aimed at constructing an efficient and stable PV prediction model. Initially, Z-score is employed to filter outliers in the PV data, and Robust STL–bilinear temporal–spectral fusion is introduced for time series feature extraction. Subsequently, an ensemble forecasting model based on bidirectional long short-term memory and extreme gradient boosting is proposed to address the limitations of existing predictive models, which suffer from low robustness and an inability to provide stable forecasts. Furthermore, to mitigate the performance degradation of the prediction model due to manual tuning, a tactics enhanced white shark optimizer is proposed for parameter optimization of the ensemble model. The optimization performance is validated using the IEEE CEC2021 test functions. Finally, the proposed method is tested on PV power generation data from a site in Shandong, China. The results demonstrate that the proposed ensemble forecasting method achieves high accuracy and exhibits strong model stability.","PeriodicalId":7619,"journal":{"name":"AIP Advances","volume":null,"pages":null},"PeriodicalIF":1.4000,"publicationDate":"2024-09-05","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":"{\"title\":\"A new method for short-term photovoltaic power generation forecast based on ensemble model\",\"authors\":\"Yunxiu Zhang, Bingxian Li, Zhiyin Han\",\"doi\":\"10.1063/5.0226761\",\"DOIUrl\":null,\"url\":null,\"abstract\":\"Photovoltaic (PV) power generation is influenced by various factors, including weather conditions, the quality of PV inverters, and the cleanliness of PV modules, with weather conditions having a particularly significant impact on power output. This paper proposes a novel method for PV power generation prediction based on an ensemble forecasting model, aimed at constructing an efficient and stable PV prediction model. Initially, Z-score is employed to filter outliers in the PV data, and Robust STL–bilinear temporal–spectral fusion is introduced for time series feature extraction. Subsequently, an ensemble forecasting model based on bidirectional long short-term memory and extreme gradient boosting is proposed to address the limitations of existing predictive models, which suffer from low robustness and an inability to provide stable forecasts. Furthermore, to mitigate the performance degradation of the prediction model due to manual tuning, a tactics enhanced white shark optimizer is proposed for parameter optimization of the ensemble model. The optimization performance is validated using the IEEE CEC2021 test functions. Finally, the proposed method is tested on PV power generation data from a site in Shandong, China. The results demonstrate that the proposed ensemble forecasting method achieves high accuracy and exhibits strong model stability.\",\"PeriodicalId\":7619,\"journal\":{\"name\":\"AIP Advances\",\"volume\":null,\"pages\":null},\"PeriodicalIF\":1.4000,\"publicationDate\":\"2024-09-05\",\"publicationTypes\":\"Journal Article\",\"fieldsOfStudy\":null,\"isOpenAccess\":false,\"openAccessPdf\":\"\",\"citationCount\":\"0\",\"resultStr\":null,\"platform\":\"Semanticscholar\",\"paperid\":null,\"PeriodicalName\":\"AIP Advances\",\"FirstCategoryId\":\"88\",\"ListUrlMain\":\"https://doi.org/10.1063/5.0226761\",\"RegionNum\":4,\"RegionCategory\":\"物理与天体物理\",\"ArticlePicture\":[],\"TitleCN\":null,\"AbstractTextCN\":null,\"PMCID\":null,\"EPubDate\":\"\",\"PubModel\":\"\",\"JCR\":\"Q4\",\"JCRName\":\"MATERIALS SCIENCE, MULTIDISCIPLINARY\",\"Score\":null,\"Total\":0}","platform":"Semanticscholar","paperid":null,"PeriodicalName":"AIP Advances","FirstCategoryId":"88","ListUrlMain":"https://doi.org/10.1063/5.0226761","RegionNum":4,"RegionCategory":"物理与天体物理","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q4","JCRName":"MATERIALS SCIENCE, MULTIDISCIPLINARY","Score":null,"Total":0}
引用次数: 0
摘要
光伏发电受多种因素的影响,包括天气条件、光伏逆变器的质量和光伏组件的清洁度,其中天气条件对发电量的影响尤为显著。本文提出了一种基于集合预测模型的新型光伏发电预测方法,旨在构建高效稳定的光伏预测模型。首先,采用 Z 分数过滤光伏数据中的异常值,并引入鲁棒 STL-双线性时间-光谱融合进行时间序列特征提取。随后,针对现有预测模型鲁棒性低和无法提供稳定预测的局限性,提出了基于双向长短期记忆和极梯度提升的集合预测模型。此外,为了减轻人工调整导致的预测模型性能下降,还提出了一种用于集合模型参数优化的战术增强型白鲨优化器。利用 IEEE CEC2021 测试功能对优化性能进行了验证。最后,在中国山东某地的光伏发电数据上测试了所提出的方法。结果表明,所提出的集合预测方法达到了很高的精度,并表现出很强的模型稳定性。
A new method for short-term photovoltaic power generation forecast based on ensemble model
Photovoltaic (PV) power generation is influenced by various factors, including weather conditions, the quality of PV inverters, and the cleanliness of PV modules, with weather conditions having a particularly significant impact on power output. This paper proposes a novel method for PV power generation prediction based on an ensemble forecasting model, aimed at constructing an efficient and stable PV prediction model. Initially, Z-score is employed to filter outliers in the PV data, and Robust STL–bilinear temporal–spectral fusion is introduced for time series feature extraction. Subsequently, an ensemble forecasting model based on bidirectional long short-term memory and extreme gradient boosting is proposed to address the limitations of existing predictive models, which suffer from low robustness and an inability to provide stable forecasts. Furthermore, to mitigate the performance degradation of the prediction model due to manual tuning, a tactics enhanced white shark optimizer is proposed for parameter optimization of the ensemble model. The optimization performance is validated using the IEEE CEC2021 test functions. Finally, the proposed method is tested on PV power generation data from a site in Shandong, China. The results demonstrate that the proposed ensemble forecasting method achieves high accuracy and exhibits strong model stability.
期刊介绍:
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