Spatiotemporal analysis and visualization of power consumption data integrated with building information models for energy savings

Chien-Cheng Chou, Cheng-Ting Chiang, Pai-Yu Wu, Chun-Ping Chu and Chia-Ying Lin

Resources, Conservation & Recycling, 2017, vol. 123, issue C, 219-229

Abstract: Using a visualization engine to display the analyze results of power consumption data in a building can provide immediate and informative feedback for energy conservation research. Previous research has demonstrated that change of residents’ behavior can facilitate achieving the net-zero energy goal for a building. This research proposed a system called iARTS (interactive Augmented Reality system for Temporal and Spatial analysis of power consumption data integrated with building information models) that was designed to: (1) integrate building information model data into power consumption data sets in order to visualize the analysis results in Unity, which is a visualization engine originally designed for game development; (2) perform a spatiotemporal analysis mechanism to help residents realize an energy-saving tip, by identifying the appliances to be turned off; (3) perform another spatiotemporal analysis mechanism to identify the appliances that can be used jointly in order to consume all the solar PV-generated electricity at a maximum; (4) provide residents with query forms, scenes retrieval functions, and animations to educate residents as to where and when to implement the aforementioned energy-saving tips. With the use of iARTS, the temporal relationships between power sockets and appliances can be accurately described along with timestamped power consumption data. Residents are expected to be able to identify the electricity usage patterns that are wasteful, as well as to see any potential adjustment plan for using as much generated electricity as possible.

Keywords: Building information modeling; Spatiotemporal analysis; Net-zero energy building; Unity game engine; Energy savings (search for similar items in EconPapers)
Date: 2017
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Persistent link: https://EconPapers.repec.org/RePEc:eee:recore:v:123:y:2017:i:c:p:219-229

DOI: 10.1016/j.resconrec.2016.03.008

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