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Found 4 papers, 1 papers with code
Learning-assisted Stochastic Capacity Expansion Planning: A Bayesian Optimization Approach
Solving large-scale capacity expansion problems (CEPs) is central to cost-effective decarbonization of regional-scale energy systems.
Learning Spatio-Temporal Aggregations for Large-Scale Capacity Expansion Problems
We evaluate aggregation outcomes over a range of hyperparameters governing the loss function and compare resulting upper bounds on the original problem with those obtained using benchmark methods.
Graph Representation Learning for Energy Demand Data: Application to Joint Energy System Planning under Emissions Constraints
Specifically, we focus on efficiently extracting a set of representative days from power and NG data in respective networks and using this set to reduce the computational burden required to solve the GTEP.
Interpretable Machine Learning Models for Modal Split Prediction in Transportation Systems
Modal split prediction in transportation networks has the potential to support network operators in managing traffic congestion and improving transit service reliability.
BIG-bench Machine Learning
Interpretable Machine Learning
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