Control of Residential Battery Charge Scheduling using Machine Learning
This thesis proposes the use of a Reinforcement Learning (RL) agent to control the charge scheduling of a residential battery system. The system consists of a house located in Sweden equipped with a photo-voltaic array and grid-connection. Real residential load data is used while the PV output is si...
| Main Author: | |
|---|---|
| Format: | Others |
| Language: | English |
| Published: |
KTH, Energiteknik
2018
|
| Subjects: | |
| Online Access: | http://urn.kb.se/resolve?urn=urn:nbn:se:kth:diva-244988 |