Big data analytics and cloud computing for sustainable building energy efficiency

J. S. Chou, N. T. Ngo, Oswald Chong, Edd Gibson

Research output: Chapter in Book/Report/Conference proceedingChapter

17 Scopus citations


Currently, big data analytics and cloud computing are emerging practices for sustainable energy systems and efficient energy management. Utilizing building energy usage data is critical for the successful deployment of energy efficiency. This chapter presents the framework of a smart decision support system (SDSS) that integrates smart grid big data analytics and cloud computing for building energy efficiency. The framework is based on a layered architecture that includes smart grid and data collection, an analytics bench, and a web-based portal. A real-world smart metering infrastructure was installed in a residential building for the experiment. The SDSS is expected to accurately identify the building energy consumption patterns and forecasted future energy usage. Moreover, end users can reduce electricity costs by using the system to optimize operation schedules of appliances, lighting systems, and heating, ventilation, and air conditioning. The proposed framework serves as a start-up creation in an application of big data analytics and cloud computing technology for sustainable building energy efficiency.

Original languageEnglish (US)
Title of host publicationStart-Up Creation: The Smart Eco-Efficient Built Environment
PublisherElsevier Inc.
Number of pages16
ISBN (Electronic)9780081005491
ISBN (Print)9780081005460
StatePublished - Jan 1 2016


  • Artificial intelligence
  • Big data analytics
  • Building energy management
  • Cloud computing
  • Data mining
  • Decision support system
  • Energy efficiency
  • Machine learning
  • Nature-inspired metaheuristic optimization
  • Pattern recognition
  • Smart grid
  • Time-series data
  • Web-based system

ASJC Scopus subject areas

  • General Medicine


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