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Deep Generative Graph Distribution Learning for Synthetic Power Grids
Power system studies require the topological structures of real-world power networks; however, such data is confidential due to important security concerns.
Energy Disaggregation via Deep Temporal Dictionary Learning
The electricity signal of each device is then modeled by a linear combination of such patterns with sparse coefficients that determine the contribution of each device in the total electricity.
Convolutional Graph Auto-encoder: A Deep Generative Neural Architecture for Probabilistic Spatio-temporal Solar Irradiance Forecasting
In this paper, a deep generative model is introduced to capture continuous probability densities corresponding to the nodes of an arbitrary graph.
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