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Found 6 papers, 1 papers with code
Direct Load Control of Thermostatically Controlled Loads Based on Sparse Observations Using Deep Reinforcement Learning
This paper considers a demand response agent that must find a near-optimal sequence of decisions based on sparse observations of its environment.
Model-Free Control of Thermostatically Controlled Loads Connected to a District Heating Network
Optimal control of thermostatically controlled loads connected to a district heating network is considered a sequential decision- making problem under uncertainty.
Convolutional Neural Networks For Automatic State-Time Feature Extraction in Reinforcement Learning Applied to Residential Load Control
Direct load control of a heterogeneous cluster of residential demand flexibility sources is a high-dimensional control problem with partial observability.
Reinforcement Learning Applied to an Electric Water Heater: From Theory to Practice
A wellknown batch reinforcement learning technique, fitted Q-iteration, is used to find a control policy, given this feature representation.
Experimental analysis of data-driven control for a building heating system
From the quantitative analysis it has been found that the control approach converges in approximately 20 days to obtain a control policy with a performance within 90% of the mathematical optimum.
Residential Demand Response Applications Using Batch Reinforcement Learning
We propose a model-free Monte-Carlo estimator method that uses a metric to construct artificial trajectories and we illustrate this method by finding the day-ahead schedule of a heat-pump thermostat.
