Cognitive Visual Analytics for Smart Energy Systems

Project Details

Description

Data are generated in hourly or sub-hourly granularity and collected on an unprecedented scale with the increasingly use of smart meters and Internet-of-Things (IoT) sensors in the energy sector. These data enable the improvement potential of smart energy systems, including optimization of energy flexibility, efficiency, fault diagnosis, model predictive control, etc. Decision makers in the energy sector are confronted with massive amounts of diverse, contradictory and dynamic information from heterogeneous sources. There is an urgent need for effective methods to exploit and discover hidden information from unexplored data sources. The project will explore a pathway that bridges the fundamental research in the area of human/computer cognitive systems and smart meter data analytics. Specifically, it comprises three gradually deepening research tasks: cognitive visual analytics to detect unusual activities and anomalies (Descriptive Analytics), to forecast energy consumption (Prediction Analytics), and to explore energy efficiency strategy (Prescriptive Analytics). The methodology will including data-driven energy modeling, visual interface design based on cognitive psychology, case study and expert study. The project will improve the state-of-the-art in energy analytics, bring advance of smart-energy modeling tools and methods that have been applied in Denmark, and lead the field internationally. The output of the project will also have an important interdisciplinary impact by exploring the possibility of cognitive process in smart energy analysis. The interdisciplinary research can produce innovative results and contribute to the EU 2020 target of R&D and innovation, and the target of climate change/energy.
AcronymCVAES
StatusActive
Effective start/end date01/10/201930/09/2021

Research Output

  • 1 Article in proceedings

VAP: A Visual Analysis Tool for Energy Consumption Spatio-temporal Pattern

Liu, X., Niu, Z., Yang, Y., Wu, J., Cheng, D. & Wang, X., 2020, Proceedings of the 23rd International Conference on Extending Database Technology. p. 579-582

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review

Open Access
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