Can Artificial Intelligence Assist Project Developers in Long-Term Management of Energy Projects? The Case of CO 2 Capture and Storage

Eric Buah, Lassi Linnanen, Huapeng Wu and Martin A. Kesse
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Eric Buah: Laboratory of Sustainability Science, School of Energy Systems, LUT University, FI-53851 Lappeenranta, Finland
Lassi Linnanen: Laboratory of Sustainability Science, School of Energy Systems, LUT University, FI-53851 Lappeenranta, Finland
Huapeng Wu: Laboratory of Intelligent Machines, School of Energy Systems, LUT University, FI-53851 Lappeenranta, Finland
Martin A. Kesse: School of Energy Systems, Mechanical Engineering Department, LUT University, FI-53851 Lappeenranta, Finland

Energies, 2020, vol. 13, issue 23, 1-15

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 CO 2 storage and the system demonstrated 90.476% performance.

Keywords: artificial intelligence; CO 2 capture and storage; deep neural network; CCS communication and engagement; fuzzy logic; fuzzy deep learning (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
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