Data Visualization in the Public Energy Sector: A Study on User Experience and Satisfaction

Abstract: This study delves into the analysis of how data visualization impacts user experience (UX) when interacting with public electrical energy data. In the era of big data, the volumes of information on energy generation, distribution, and consumption are expanding exponentially. This surge underscores the urgency for presentation techniques that not only simplify complex datasets for the lay audience but also improve engagement and comprehension. Thus, the study aims to bridge the existing knowledge gap by identifying effective data visualization strategies that enhance the public’s ability to understand intricate data, thereby supporting informed decision-making and heightening awareness about energy sustainability. Adopting a mixed-methods approach, the study integrates extensive literature reviews with empirical usability testing involving 30 participants. To complement the quantitative findings, qualitative insights were extracted from interviews and focus groups, aiming to capture user preferences, challenges encountered, and suggestions for improvement. This analysis covered the effectiveness of various visualization components, including filters, information hierarchies, graphical elements, and data diversity, in facilitating an intuitive grasp of electrical energy data. The study showed a correlation between intuitive visualization techniques and the improvement of UX metrics such as engagement, comprehension, and satisfaction. Key findings emphasized that features such as interactive filters and good information hierarchies are instrumental in empowering users to effectively navigate and interpret electrical energy data. The study culminates in the formulation of eleven targeted guidelines for the development of user-centric data visualizations within the public energy sector.

Keywords: Data visualization, Human Factors, Cognitive Psychology, User Experience, Public Energy Data

DOI: 10.54941/ahfe1005501

Cite this paper:

​