Title: Motivations behind P2P energy trading: a machine learning approach

Authors: Shan Shan; Yulei Li; Honglei Li

Addresses: School of Strategy and Leadership, Coventry University, UK ' Business School, Durham University, Durham, UK ' School of Computer and Information Science, Northumbria University, Newcastle Upon Tyne, UK

Abstract: 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.

Keywords: P2P energy trading; collaborative consumption; motivations; natural language processing; NLP; latent Dirichlet process; LDA; customer reviews.

DOI: 10.1504/IJCCM.2022.123645

International Journal of Chinese Culture and Management, 2022 Vol.5 No.3, pp.189 - 202

Received: 28 Jun 2020
Accepted: 28 Oct 2020

Published online: 30 Jun 2022 *

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