Semi-supervised Energy Modeling (SSEM) for Building Clusters Using Machine Learning Techniques

Hariharan Naganathan, Oswald Chong, Xue Wen Chen

Research output: Chapter in Book/Report/Conference proceedingConference contribution

3 Scopus citations


There are many data mining and machine learning techniques to manage large sets of complex energy supply and demand data for building, organization and city. As the amount of data continues to grow, new data analysis methods are needed to address the increasing complexity. Using data from the energy loss between the supply (energy production sources) and demand (buildings and cities consumption), this paper proposes a Semi-Supervised Energy Model (SSEM) to analyse different loss factors for a building cluster. This is done by deep machine learning by training machines to semi-supervise the learning, understanding and manage the process of energy losses. Semi-Supervised Energy Model (SSEM) aims at understanding the demand-supply characteristics of a building cluster and utilizes the confident unlabelled data (loss factors) using deep machine learning techniques. The research findings involves sample data from one of the university campuses and presents the output, which provides an estimate of losses that can be reduced. The paper also provides a list of loss factors that contributes to the total losses and suggests a threshold value for each loss factor, which is determined through real time experiments. The conclusion of this paper provides a proposed energy model that can provide accurate numbers on energy demand, which in turn helps the suppliers to adopt such a model to optimize their supply strategies.

Original languageEnglish (US)
Title of host publicationProcedia Engineering
PublisherElsevier Ltd
Number of pages6
StatePublished - 2015
EventInternational Conference on Sustainable Design, Engineering and Construction, ICSDEC 2015 - Chicago, United States
Duration: May 10 2015May 13 2015


OtherInternational Conference on Sustainable Design, Engineering and Construction, ICSDEC 2015
Country/TerritoryUnited States


  • Demand- supply analysis
  • Energy losses
  • Energy Modeling
  • Labelled and Unlabelled factors
  • Semi-supervised learning

ASJC Scopus subject areas

  • Engineering(all)


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