Search Results for author:
Found 4 papers, 2 papers with code
AI-Powered Predictions for Electricity Load in Prosumer Communities
The flexibility in electricity consumption and production in communities of residential buildings, including those with renewable energy sources and energy storage (a. k. a., prosumers), can effectively be utilized through the advancement of short-term demand response mechanisms.
Hierarchical Framework for Interpretable and Probabilistic Model-Based Safe Reinforcement Learning
Deep reinforcement learning has been the pioneer for solving this problem without the need for relying on the physical model of complex systems by just interacting with it.
Ranked #1 on
Decision Making
on NASA C-MAPSS
Specialized Deep Residual Policy Safe Reinforcement Learning-Based Controller for Complex and Continuous State-Action Spaces
Deep reinforcement learning has the potential to address these problems by learning optimal control policies through exploration in an environment.
An Evolutionary Stochastic-Local-Search Framework for One-Dimensional Cutting-Stock Problems
We introduce an evolutionary stochastic-local-search (SLS) algorithm for addressing a generalized version of the so-called 1/V/D/R cutting-stock problem.
