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Household Energy Cost Optimization Using Deep Reinforcement Learning

Terekhova, Anna LU and Fang, Jade Yuwei LU (2022) DABN01 20221
Department of Statistics
Department of Economics
Abstract
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 thermodynamic behavior of an existing building, making it more cost-effective than using building simulation software. Using the final Deep Q-Network model, a cost reduction of up to 25% was achieved.
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author
Terekhova, Anna LU and Fang, Jade Yuwei LU
supervisor
organization
course
DABN01 20221
year
type
H1 - Master's Degree (One Year)
subject
keywords
Energy optimization, deep reinforcement learning, sensor data, neural network, indoor heating, DQN, energy costs
language
English
id
9085102
date added to LUP
2022-06-08 12:51:41
date last changed
2022-10-10 16:05:35
@misc{9085102,
  abstract     = {{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 thermodynamic behavior of an existing building, making it more cost-effective than using building simulation software. Using the final Deep Q-Network model, a cost reduction of up to 25% was achieved.}},
  author       = {{Terekhova, Anna and Fang, Jade Yuwei}},
  language     = {{eng}},
  note         = {{Student Paper}},
  title        = {{Household Energy Cost Optimization Using Deep Reinforcement Learning}},
  year         = {{2022}},
}