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.
| Original language | English |
|---|---|
| Pages (from-to) | 1-12 |
| Number of pages | 12 |
| Journal | Proceedings of the International Conference on Electronic Business (ICEB) |
| Publication status | Published - 31 Dec 2019 |
| Event | 19th International Conference on Electronic Business, ICEB 2019 - Newcastle upon Tyne, United Kingdom Duration: 8 Dec 2019 → 12 Dec 2019 |
Keywords
- Data mining
- Feature engineering
- HLDA
- Peer to Peer energy trading