Motivations behind P2P energy trading: a machine learning approach

Peer to peer (P2P) energy trading as an emerging project of collaborative consumption has attracted interests and attention from recent research. Previous research has paid attention to business models, operation process, but neglected the motivations behind the mechanism of P2P energy trading. At the same time, how to design a peer to peer energy trading platform with selected features thus becomes vital in facilitating user trading experience. This study will use the natural language processing (NLP) method to assess characteristics that influence P2P energy trading. Notably, the data in this study will be collected from Twitter and reviews of Vandebron by using the latent Dirichlet process (LDA) model with Python.