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Found 5 papers, 2 papers with code
An adaptive safety layer with hard constraints for safe reinforcement learning in multi-energy management systems
(II) introducing self-improving hard constraints, to increase the accuracy of the constraint functions as more and new data becomes available so that better policies can be learnt.
TreeC: a method to generate interpretable energy management systems using a metaheuristic algorithm
This method learns the decision strategy of the EMS based on historical data contrary to RBC and MPC approaches that are typically considered as non adaptive solutions.
Safe reinforcement learning for multi-energy management systems with known constraint functions
Reinforcement learning (RL) is a promising optimal control technique for multi-energy management systems.
Evolutionary scheduling of university activities based on consumption forecasts to minimise electricity costs
The forecasts obtained are used as the base load for the schedule optimisation of university activities and battery usage.
Model-predictive control and reinforcement learning in multi-energy system case studies
Model-predictive-control (MPC) offers an optimal control technique to establish and ensure that the total operation cost of multi-energy systems remains at a minimum while fulfilling all system constraints.
