In distributed photovoltaic (PV) power generation systems, data quality plays a critical role in the accuracy of predictive models. However, the complexity of distributed PV sensor data, including issues such as missi...
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Traffic prediction is an important and yet highly challenging problem due to the complexity and constantly changing nature of traffic systems. To address the challenges, we propose a graph and attentive multi-path con...
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Traffic prediction is an important and yet highly challenging problem due to the complexity and constantly changing nature of traffic systems. To address the challenges, we propose a graph and attentive multi-path convolutional network (GAMCN) model to predict traffic conditions such as traffic speed across a given road network into the future. Our model focuses on the spatial and temporal factors that impact traffic conditions. To model the spatial factors, we propose a variant of the graph convolutional network (GCN) named LPGCN to embed road network graph vertices into a latent space, where vertices with correlated traffic conditions are close to each other. To model the temporal factors, we use a multi-path convolutional neural network (CNN) to learn the joint impact of different combinations of past traffic conditions on the future traffic conditions. Such a joint impact is further modulated by an attention generated from an embedding of the prediction time, which encodes the periodic patterns of traffic conditions. We evaluate our model on real-world road networks and traffic data. The experimental results show that our model outperforms state-of-art traffic prediction models by up to 18.9% in terms of prediction errors and 23.4% in terms of prediction efficiency.
Accurate wind speed forecasting can help ensure power-system stability. Many previous studies often neglect spatio-temporal dependence. Therefore, effectively modeling the complex and dynamic spatio-temporal correlati...
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Accurate wind speed forecasting can help ensure power-system stability. Many previous studies often neglect spatio-temporal dependence. Therefore, effectively modeling the complex and dynamic spatio-temporalcorrelations (STCs) between spatially distributed wind speeds and extracting informative spatio-temporal features is very important for boosting forecast accuracy. This study proposes a novel sparse and dynamic graph-based spatio-temporal wind speed forecasting method with local-global features (LGFs). First, a dynamic STC modeling block is designed to learn the dynamic STC degree based on the similarity of wind temporal characteristics. To reduce computational costs, a threshold is set to select the most highly correlated neighboring sites, resulting in a sparse graph. Then, a parallel-structured LGF extraction block including a local feature extraction module and a global feature extraction module is developed. It can capture local features for a single site and global features representing spatio-temporal dependence among neighbor sites according to the obtained graph. The obtained features are fused into the comprehensive LGFs. Finally, accurate wind speed forecasts for multiple sites are generated simultaneously. The proposed model is tested using numerous benchmark models, including temporal, spatio-temporal, static graph-based, and complete graph-based models. The results show that it can effectively learn dynamic STCs and attain the highest accuracy.
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