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Found 15 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.
Cost-effective Planning of Decarbonized Power-Gas Infrastructure to Meet the Challenges of Heating Electrification
Applying the framework to study the U. S. New England region in 2050 across 20 weather scenarios, we find high electrification of the residential sector can increase sectoral peak and total electricity demands by up to 56-158% and 41-59% respectively relative to business-as-usual projections.
Uncertainty Informed Optimal Resource Allocation with Gaussian Process based Bayesian Inference
(2) How can we computationally handle both nonlinear ODE constraints and parameter uncertainties for a generic stochastic optimization problem for resource allocation?
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.
Effective Dimension in Bandit Problems under Censorship
In this paper, we study both multi-armed and contextual bandit problems in censored environments.
Electric-Gas Infrastructure Planning for Deep Decarbonization of Energy Systems
The transition to a deeply decarbonized energy system requires coordinated planning of infrastructure investments and operations serving multiple end-uses while considering technology and policy-enabled interactions across sectors.
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.
Green Routing Game: Strategic Logistical Planning using Mixed Fleets of ICEVs and EVs
Each player faces the cost of delayed delivery (due to charging requirements of EVs) and a pollution cost levied on the ICEVs.
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
+1
Damage Estimation and Localization from Sparse Aerial Imagery
The approach is based on using structure from motion to relate image coordinates to world coordinates via a projective transformation, using class activation mapping to detect the extent of damage in an image, and applying the projective transformation to localize damage in world coordinates.
Integer Programming-based Error-Correcting Output Code Design for Robust Classification
We also estimate the adversarial accuracy of our ECOC-based classifiers in a white-box setting.
Multi-agent Bayesian Learning with Adaptive Strategies: Convergence and Stability
Any fixed point belief consistently estimates the payoff distribution given the fixed point strategy profile.
Bayesian Learning with Adaptive Load Allocation Strategies
We study a Bayesian learning dynamics induced by agents who repeatedly allocate loads on a set of resources based on their belief of an unknown parameter that affects the cost distributions of resources.
Semantic Analysis of Traffic Camera Data: Topic Signal Extraction and Anomalous Event Detection
In this article, we propose a semantics-oriented approach to analyzing sequential image data, and demonstrate its application for automatic detection of real-world, anomalous events in weather and traffic conditions.
Semantic Topic Analysis of Traffic Camera Images
This study demonstrates the flexibility of our approach, which allows us to analyze weather events and freeway traffic using only traffic camera image labels.
