AI-based smart home energy management
With the increasing request for saving energy and protecting environment, the concept of smart home energy management (SHEM) is developed and applied in our daily life. In this dissertation, the framework of smart home is firstly introduced, and some popular methods of SHEM, e.g., parsimonious rando...
| Հիմնական հեղինակ: | |
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| Այլ հեղինակներ: | |
| Ձևաչափ: | Thesis-Master by Coursework |
| Լեզու: | English |
| Հրապարակվել է: |
Nanyang Technological University
2022
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| Խորագրեր: | |
| Առցանց հասանելիություն: | https://hdl.handle.net/10356/163313 |
| _version_ | 1826114248658386944 |
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| author | Zhou, Xueni |
| author2 | Xu Yan |
| author_facet | Xu Yan Zhou, Xueni |
| author_sort | Zhou, Xueni |
| collection | NTU |
| description | With the increasing request for saving energy and protecting environment, the concept of smart home energy management (SHEM) is developed and applied in our daily life. In this dissertation, the framework of smart home is firstly introduced, and some popular methods of SHEM, e.g., parsimonious random time series, regression or causal models and artificial intelligent based models are reviewed. Then, the methodology on forecasting electricity price is mentioned, such as data collection and pre-processing techniques. Subsequently, a DDPG based model for SHEM is proposed, where the specifical summary and mechanism are mentioned as well. Through the offline training and online testing, three different models of smart home are considered and simulated. The training results denote that the reward increases as the moving of step, while the testing results prove it as well, which means the DDPG based model proposed is effective and accurate. |
| first_indexed | 2024-10-01T03:36:04Z |
| format | Thesis-Master by Coursework |
| id | ntu-10356/163313 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T03:36:04Z |
| publishDate | 2022 |
| publisher | Nanyang Technological University |
| record_format | dspace |
| spelling | ntu-10356/1633132022-12-02T00:35:01Z AI-based smart home energy management Zhou, Xueni Xu Yan School of Electrical and Electronic Engineering xuyan@ntu.edu.sg Engineering::Electrical and electronic engineering With the increasing request for saving energy and protecting environment, the concept of smart home energy management (SHEM) is developed and applied in our daily life. In this dissertation, the framework of smart home is firstly introduced, and some popular methods of SHEM, e.g., parsimonious random time series, regression or causal models and artificial intelligent based models are reviewed. Then, the methodology on forecasting electricity price is mentioned, such as data collection and pre-processing techniques. Subsequently, a DDPG based model for SHEM is proposed, where the specifical summary and mechanism are mentioned as well. Through the offline training and online testing, three different models of smart home are considered and simulated. The training results denote that the reward increases as the moving of step, while the testing results prove it as well, which means the DDPG based model proposed is effective and accurate. Master of Science (Power Engineering) 2022-12-02T00:35:00Z 2022-12-02T00:35:00Z 2022 Thesis-Master by Coursework Zhou, X. (2022). AI-based smart home energy management. Master's thesis, Nanyang Technological University, Singapore. https://hdl.handle.net/10356/163313 https://hdl.handle.net/10356/163313 en application/pdf Nanyang Technological University |
| spellingShingle | Engineering::Electrical and electronic engineering Zhou, Xueni AI-based smart home energy management |
| title | AI-based smart home energy management |
| title_full | AI-based smart home energy management |
| title_fullStr | AI-based smart home energy management |
| title_full_unstemmed | AI-based smart home energy management |
| title_short | AI-based smart home energy management |
| title_sort | ai based smart home energy management |
| topic | Engineering::Electrical and electronic engineering |
| url | https://hdl.handle.net/10356/163313 |
| work_keys_str_mv | AT zhouxueni aibasedsmarthomeenergymanagement |