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- 摘要
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- 实验方案
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Multi-Site Photovoltaic Forecasting Exploiting Space-Time Convolutional Neural Network
摘要： The accurate forecasting of photovoltaic (PV) power generation is critical for smart grids and the renewable energy market. In this paper, we propose a novel short-term PV forecasting technique called the space-time convolutional neural network (STCNN) that exploits the location information of multiple PV sites and historical PV generation data. The proposed structure is simple but effective for multi-site PV forecasting. In doing this, we propose a greedy adjoining algorithm to preprocess PV data into a space-time matrix that captures spatio-temporal correlation, which is learned by a convolutional neural network. Extensive experiments with multi-site PV generation from three typical states in the US (California, New York, and Alabama) show that the proposed STCNN outperforms the conventional methods by up to 33% and achieves fairly accurate PV forecasting, e.g., 4.6–5.3% of the mean absolute percentage error for a 6 h forecasting horizon. We also investigate the effect of PV sites aggregation for virtual power plants where errors from some sites can be compensated by other sites. The proposed STCNN shows substantial error reduction by up to 40% when multiple PV sites are aggregated.
关键词： CNN,spatio-temporal correlation,multi-site photovoltaic forecasting,space-time matrix
更新于2025-09-11 14:15:04
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SPACE-TIME MATRIX METHOD FOR MIXED NEAR-FIELD AND FAR-FIELD SOURCES LOCALIZATION
摘要： Mixed near-?eld and far-?eld sources localization problem has received signi?cant attention recently in some practical applications, such as speaker localization using microphone arrays and guidance systems, etc. This paper presents a novel space-time matrix method to localize mixed near-?eld and far-?eld sources. Using the proposed method, both the direction-of-arrival (DOA) and range of a source can be estimated by the same eigen-pair of a de?ned space-time matrix. Therefore, the pairing of the estimated angles and ranges is automatically determined. Compared with the previous work, the presented method o?ers a number of advantages over other recently proposed algorithms. For example, it can avoid not only parameters matching problem but also aperture loss problem. It has lower computational complexity since the proposed method does not require the high-order statistics or any parameter search. Simulation results show the performance of the proposed algorithm.
关键词： DOA estimation,range estimation,space-time matrix method,mixed near-?eld and far-?eld sources localization
更新于2025-09-04 15:30:14