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Found 5 papers, 2 papers with code
Adaptive Probabilistic Forecasting of Electricity (Net-)Load
The proliferation of local generation, demand response, and electrification of heat and transport are changing the fundamental drivers of electricity load and increasing the complexity of load modelling and forecasting.
Robust Neural Posterior Estimation and Statistical Model Criticism
In this work we revisit neural posterior estimation (NPE), a class of algorithms that enable black-box parameter inference in simulation models, and consider the implication of a simulation-to-reality gap.
Daily peak electrical load forecasting with a multi-resolution approach
In the context of smart grids and load balancing, daily peak load forecasting has become a critical activity for stakeholders of the energy industry.
Additive stacking for disaggregate electricity demand forecasting
We focus on forecasting demand at the individual household level, which is more challenging than forecasting aggregate demand, due to the lower signal-to-noise ratio and to the heterogeneity of consumption patterns across households.
An Extended Empirical Saddlepoint Approximation for Intractable Likelihoods
The challenges posed by complex stochastic models used in computational ecology, biology and genetics have stimulated the development of approximate approaches to statistical inference.
Methodology Applications
