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Found 10 papers, 0 papers with code
Efficient Data-Driven MPC for Demand Response of Commercial Buildings
Model predictive control (MPC) has been shown to significantly improve the energy efficiency of buildings while maintaining thermal comfort.
Tensor-based Space Debris Detection for Satellite Mega-constellations
The analysis reveals that the reflected signals of the THz can be utilized for the detection of space debris.
Evolution of High Throughput Satellite Systems: Vision, Requirements, and Key Technologies
High throughput satellites (HTS), with their digital payload technology, are expected to play a key role as enablers of the upcoming 6G networks.
Online Dynamic Submodular Optimization
We propose the online submodular greedy algorithm (OSGA) which solves to optimality an approximation of the previous round loss function to avoid the NP-hardness of the original problem.
Multi-Agent Reinforcement Learning for Fast-Timescale Demand Response of Residential Loads
To integrate high amounts of renewable energy resources, electrical power grids must be able to cope with high amplitude, fast timescale variations in power generation.
Multi-agent Reinforcement Learning
reinforcement-learning
+1
Approximated Multi-Agent Fitted Q Iteration
We evaluate the performance of AMAFQI and compare it to FQI in numerical simulations.
Online Convex Optimization with Binary Constraints
Then, we derive a finite-time bound that is sublinear in time and linear in the cumulative variation of the relaxed, continuous round optima.
Dynamic and Distributed Online Convex Optimization for Demand Response of Commercial Buildings
We extend the regret analysis of the online distributed weighted dual averaging (DWDA) algorithm [1] to the dynamic setting and provide the tightest dynamic regret bound known to date with respect to the time horizon for a distributed online convex optimization (OCO) algorithm.
Predictive Online Convex Optimization
We show that under these conditions and without any assumptions on the predictability of the environment, the predictive update strictly improves on the performance of the standard update.
Setpoint Tracking with Partially Observed Loads
We use online convex optimization (OCO) for setpoint tracking with uncertain, flexible loads.
