A considerable amount of studies report that negative emotions evoked by Wind Energy, Nuclear Energy and CO2 Capture and Storage (CCS) can lead to cancellation of the energy project or a delay in policy decisions for its implementation if not adequately addressed. Earlier studies have attempted to study this problem using self-reported emotion measurements to identify the emotions the participants felt. As an alternative, we propose the use of an emotional artificial intelligence (AI) algorithm for improved modelling and prediction of the participants' emotional behaviour to guide decision-making. We have validated the system using emotional responses to a hypothetical CCS project as a case study. Running our simulation on the experimental dataset (thus 40% of the 72,105), we obtained an average validation accuracy of 98.81%.We challenged the algorithm further with 84 test samples (unseen cases), and it predicted 75 feelings correctly when the stakeholders took a definite position on how they felt. Although there are few limitations to this study, we did find, in a sensitivity experiment, that it was challenging for the algorithm to predict indecisive feelings. The method is adaptable to study emotional responses to other projects, including Wind Energy, Nuclear Energy and Hydrogen Technology.
Recent developments in artificial intelligence (AI) modeling tools allows for envisaging that AI will remove elements of human mechanical effort from welding operations. This paper contributes to this development by proposing an AI tungsten inert gas (TIG) welding algorithm that can assist human welders to select desirable end factors to achieve good weld quality in the welding process. To demonstrate its feasibility, the proposed model has been tested with data from 27 experiments using current, arc length and welding speed as control parameters to predict weld bead width. A fuzzy deep neural network, which is a combination of fuzzy logic and deep neural network approaches, is applied in the algorithm. Simulations were carried out on an experimental test dataset with the AI TIG welding algorithm. The results showed 92.59% predictive accuracy (25 out of 27 correct answers) as compared to the results from the experiment. The performance of the algorithm at this nascent stage demonstrates the feasibility of the proposed method. This performance shows that in future work, if its predictive accuracy is improved with human input and more data, it could achieve the level of accuracy that could support the human welder in the field to enhance efficiency in the welding process. The findings are useful for industries that are in the welding trade and serve as an educational tool.
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.
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