Abstract
The emergence of the Peer-to-Peer (P2P) energy trading platforms provides a new method for the general public to use and
trade green energy. How to design the peer to peer energy trading platform thus becomes important in facilitating user trading
experience. This study will use the data mining method to evaluate factors impacting P2P energy trading experience. Python
was used to analyze data extracted from Twitter and Natural Language Processing (NLP) method was implemented with
hierarchical Latent Dirichlet Process (hLDA) model. . The study’s findings will be examined in detail.
trade green energy. How to design the peer to peer energy trading platform thus becomes important in facilitating user trading
experience. This study will use the data mining method to evaluate factors impacting P2P energy trading experience. Python
was used to analyze data extracted from Twitter and Natural Language Processing (NLP) method was implemented with
hierarchical Latent Dirichlet Process (hLDA) model. . The study’s findings will be examined in detail.
| Original language | English |
|---|---|
| Title of host publication | Proceedings of The 19th International Conference on Electronic Business |
| Editors | Eldon Y. Li, Honglei Li |
| Publisher | ICEB |
| Pages | 1-12 |
| Number of pages | 12 |
| Publication status | Published - Dec 2019 |
| Externally published | Yes |
| Event | The 19th International Conference on Electronic Business - Hilton, Newcastle upon Tyne, United Kingdom Duration: 8 Dec 2019 → 12 Dec 2019 |
Publication series
| Name | |
|---|---|
| ISSN (Electronic) | 1683-0040 |
Conference
| Conference | The 19th International Conference on Electronic Business |
|---|---|
| Country/Territory | United Kingdom |
| City | Newcastle upon Tyne |
| Period | 8/12/19 → 12/12/19 |
Keywords
- Peer to Peer energy trading
- data mining
- hLDA
- feature engineering