The growing demand for renewable energy sources like wind and solar power requires accurate and reliable forecasting techniques for effective planning and operation. This study presents an attention-basedspatial- tem...
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The growing demand for renewable energy sources like wind and solar power requires accurate and reliable forecasting techniques for effective planning and operation. This study presents an attention-basedspatial- temporalgraphneuralnetwork-long short-term memory (ASTGNN-LSTM) model designed to predict wind speed and solar radiation using 20 years of meteorological data from five regions in Northwest China. The ASTGNNLSTM model shows significant performance improvements over traditional methods, such as the historical average model, autoregressive integrated moving average model, and graph convolutional network with LSTM. After optimizing the hidden layers and learning rate, the relative errors for predicting wind speed and solar radiation are reduced to 27.15 % and 6.11 %, respectively. Sensitivity analysis reveals that location data have the most significant impact on predictions. These findings demonstrate that the ASTGNN-LSTM model effectively captures nonlinear relationships and can enhance renewable energy planning and management.
The rapid evolution of Intelligent Transportation Systems (ITS) in the Big Data era, propelled by the Internet of Things (IoT), has led to advanced data-driven vehicle traffic forecasting. graphneuralnetworks (GNNs)...
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ISBN:
(纸本)9781728190549
The rapid evolution of Intelligent Transportation Systems (ITS) in the Big Data era, propelled by the Internet of Things (IoT), has led to advanced data-driven vehicle traffic forecasting. graphneuralnetworks (GNNs), particularly the attention-based spatial-temporal graph neural networks (ASTGNN), are promising in traffic forecasting but face limitations in integrating Big Data with privacy-preserving Federated Learning (FL) due to unique data topology processing. This paper introduces the Availability Aware Federated attention-based spatial-temporal graph neural network (FastFlow), an innovative framework that enhances ASTGNN by integrating Federated Learning across entities and employing Big Data methodologies. FastFlow's distinctiveness lies in its availability-aware approach, aggregating adjacency matrices for global topology and utilizing a novel communication protocol that prioritizes data availability and correlation among organizations. Our evaluation of Caltrans Performance Measurement System (PEMS) data in a simulated setup demonstrates FastFlow's ability to balance predictive accuracy and data security in a multi-organizational context.
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