Household Energy Cost Optimization Using Deep Reinforcement Learning
(2022) DABN01 20221Department 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.
Please use this url to cite or link to this publication:
http://lup.lub.lu.se/student-papers/record/9085102
- author
- Terekhova, Anna LU and Fang, Jade Yuwei LU
- supervisor
- organization
- course
- DABN01 20221
- year
- 2022
- 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}}, }