Matches, Mismatches, and Methods: Multiple-View Workflows for Energy Portfolio Analysis

Area of Science:

• Data Visualization
• Energy Systems Analysis
• Human-Computer Interaction

Background:

• Current energy analysis tools lack scalability and present data at an overly aggregated level, hindering effective monitoring of large building portfolios.
• Analyzing energy performance across extensive building portfolios presents significant challenges due to data complexity and limitations of existing analytical methods.

Purpose of the Study:

• To design and evaluate novel visualization techniques for scalable energy performance analysis in large building portfolios.
• To address research questions concerning scalability, view coordination, and the suitability of different chart types for complex energy data.

Main Methods:

• Conducted a visualization design study involving work domain analysis and data/task abstraction.
• Developed generalizable visual encoding strategies for time-oriented energy data, focusing on matches and mismatches.
• Investigated workflow design considerations and methodological aspects of visualization design studies.

Main Results:

• Developed scalable visualization designs that overcome limitations of current tools for analyzing large building energy data.
• Demonstrated the inappropriateness of line charts for derived and aggregated energy data, proposing alternatives.
• Provided guidelines for visualization design, emphasizing familiarity, trust, and methodological rigor.

Conclusions:

• The developed visualization designs offer a scalable solution for analyzing and monitoring energy performance in large building portfolios.
• These findings inform the creation of more effective energy analysis software, enhancing usability for thousands of energy workers.
• The study provides a framework for future visualization design research in complex data domains.