A1 Journal article (refereed), original research

Can Artificial Intelligence Assist Project Developers in Long-Term Management of Energy Projects? The Case of CO2 Capture and Storage


Open Access publication

Publication Details
Authors: Buah Eric, Linnanen Lassi, Wu Huapeng, Kesse Martin
Publisher: MDPI
Publication year: 2020
Language: English
Related Journal or Series Information: Energies
Volume number: 13
Issue number: 23
ISSN: 1996-1073
JUFO-Level of this publication: 1
Open Access: Open Access publication

Abstract

This paper contributes to the state of the art of applications of artificial intelligence (AI) in energy systems with a focus on the phenomenon of social acceptance of energy projects. The aim of the paper is to present a novel AI-powered communication and engagement framework for energy projects. The method can assist project managers of energy projects to develop AI-powered virtual communication and engagement agents for engaging their citizens and their network of stakeholders who influence their energy projects. Unlike the standard consultation techniques and large-scale deliberative engagement approaches that require face-to-face engagement, the virtual engagement platform provides citizens with a forum to continually influence project outcomes at the comfort of their homes or anywhere via mobile devices. In the communication and engagement process, the project managers’ cognitive capability can be augmented with the probabilistic capability of the algorithm to gain insights into the stakeholders’ positive and negative feelings on the project, in order to devise interventions to co-develop an acceptable energy project. The proposed method was developed using the combined capability of fuzzy logic and a deep neural network incorporated with a Likert scaling strategy to reason with and engage people. In a mainstream deep neural network, one requires lots of data to build the system. The novelty of our system, however, in relation to the mainstream deep neural network approach, is that one can even use small data of a few hundreds to build the system. Further, its performance can be improved over time as it learns more about the future. We have tested the feasibility of the system using citizens’ affective responses to CO2 storage and the system demonstrated 90.476% performance.


Last updated on 2020-30-11 at 14:20