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Household Energy Cost Optimization Using Deep Reinforcement Learning

Magister-uppsats

Terekhova, Anna, Fang, Jade Yuwei

Lunds universitet/Statistiska institutionen

Publicerad: 2022

Språk: Engelska

Nyckelord

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⌕Business and Economics⌕Neural network⌕Deep Reinforcement Learning⌕Sensor Data⌕Energy optimization⌕DQN⌕energy costs⌕indoor heating

Sammanfattning

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.

Information

Författare
Terekhova, Anna, Fang, Jade Yuwei
Lärosäte / institution
Lunds universitet/Statistiska institutionen
Publiceringsdatum
2022
Uppsatstyp
Magister-uppsats
Språk
Engelska
Nyckelord
⌕Business and Economics⌕Neural network⌕Deep Reinforcement Learning⌕Sensor Data⌕Energy optimization⌕DQN⌕energy costs⌕indoor heating

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