Household Energy Cost Optimization Using Deep Reinforcement Learning
This thesis aims to address the rising energy costs by using IoT technology and reinforcement learning. We use historical sensor data to fit a deep reinforcement learning model that is capable of optimizing the control of a heating system in a way that minimizes energy costs, while maintaining a comfortable indoor temperature. This model-free approach uses neural networks to simulate the thermodyn