Day-Ahead Probabilistic Forecasting of Net-Load and Demand Response Potentials with High Penetration of Behind-the-Meter Solar-plus-Storage

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With the increasing penetration of behind-the-meter solar and energy storage, it is favored to leverage recent advances in artificial intelligence to enhance the accuracy of net-load forecasting, the observability of net-load variability, and the understanding of the coupling between net-load and demand response potentials. The proposed project will develop two models to address the hybrid probabilistic forecasting when small and large data sets are available. The first model will incorporate a new gradient boosting machine, in which a projection of the distribution into a Riemannian space is considered, whose corresponding natural gradient is expected to give better updates at each iteration than the state of the art. Meanwhile, a data-driven type-2 fuzzy system which generates monotone if-then rules will be developed to preprocess inputs. The second model consists of graph attention networks, transformers, and variational autoencoders. The graph attention networks overcome the theoretical issues with spectral based methods. The transformers ensure each time step to attend over all the time steps in the input sequence, compared with recurrent neural networks. The combination can give better spatiotemporal information. Moreover, those two models will be extended to forecast net-load with the consideration of demand response potentials, as a multi-target forecasting task.