| MEI Bingxiao (梅冰笑),MA Lyubin,YIN Jie,XIE Zhiduo,WANG Feng.[J].高技术通讯(英文),2025,31(3):288~299 |
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| A short-term photovoltaic power prediction method based on improved spectral clustering-DTW and Stacking fusion |
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| DOI：10. 3772 / j. issn. 1006-6748. 2025. 03. 009 |
| 中文关键词: |
| 英文关键词: photovoltaic output prediction, feature dimension optimization, recursive feature selection, spectral clustering-dynamic time warping, stacking |
| 基金项目: |
| Author Name | Affiliation | | MEI Bingxiao (梅冰笑) | (Zhejiang Huayun Information Technology Co. , Ltd. , Hangzhou 310012, P. R. China) | | MA Lyubin | | | YIN Jie | | | XIE Zhiduo | | | WANG Feng | |
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| 中文摘要: |
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| 英文摘要: |
| Accurate short-term photovoltaic (PV) output forecasting is beneficial for increasing grid stability and enhancing the capacity for photovoltaic power absorption. In response to the challenges faced by commonly used photovoltaic forecasting methods, which struggle to handle issues such as non-uniform lengths of time series data for power generation and meteorological conditions, overlapping photovoltaic characteristics, and nonlinear correlations, an improved method that utilizes spectral clustering and dynamic time warping (DTW) for selecting similar days is proposed to optimize the dataset along the temporal dimension. Furthermore, XGBoost is employed for recursive feature selection. On this basis, to address the issue that single forecasting models excel at capturing different data characteristics and tend to exhibit significant prediction errors under adverse meteorological conditions, an improved forecasting model based on Stacking and weighted fusion is proposed to reduce the independent bias and variance of individual models and enhance the predictive accuracy. Finally, experimental validation is carried out using real data from a photovoltaic power station in the Xiaoshan District of Hangzhou, China, demonstrating that the proposed method can still achieve accurate and robust forecasting results even under conditions of significant meteorological fluctuations |
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