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Related Experiment Videos

Investigating energy-saving potentials in the cloud.

Da-Sheng Lee1

  • 1Department of Energy and Refrigerating Air-Conditioning Engineering, National Taipei University of Technology, No. 1, Sec. 3, Chung-Hsiao E. Rd., Taipei 106, Taiwan. f11167@ntut.edu.tw.

Sensors (Basel, Switzerland)
|February 25, 2014
PubMed
Summary
This summary is machine-generated.

A cloud sensor system effectively assesses building energy-saving potential by analyzing online data. This method accurately identifies stores with low energy-saving potential, reducing the need for costly site visits.

Related Experiment Videos

Area of Science:

  • Building energy efficiency
  • Sustainable energy management
  • Data analytics in environmental science

Background:

  • Assessing building energy-saving potential is crucial for sustainability.
  • Traditional methods can be resource-intensive and time-consuming.
  • Web data offers a novel, scalable approach to energy analysis.

Purpose of the Study:

  • To develop and validate a cloud sensor system for determining building energy-saving potential.
  • To leverage online data for efficient energy use index (EUI) calculation.
  • To identify stores with similar characteristics for comparative energy analysis.

Main Methods:

  • A cloud sensor system was developed to collect online data from stores.
  • Energy usage records were automatically surveyed from power company websites.
  • An 8-dimensional data array was constructed using web data from 67 stores.
  • Similarity conditions (capital, scale, weather, web exposure) were used to group stores.
  • Energy-saving potential was determined by calculating EUI differences and technical efficiency.

Main Results:

  • A case study involving 67 stores and 33 site visits demonstrated high agreement between cloud data and site investigations (within 4.17% error).
  • The system successfully identified eight stores with low energy-saving potential (<5%).
  • The cloud sensor system proved effective in preemptively identifying low-potential candidates, optimizing resource allocation.

Conclusions:

  • The developed cloud sensor system provides a reliable and cost-effective method for assessing building energy-saving potential.
  • This approach significantly reduces the need for physical site visits by accurately identifying energy-saving opportunities.
  • The system offers a scalable solution for energy management across numerous commercial buildings.