Multiple electric energy consumption forecasting using a cluster-based strategy for transfer learning in smart building

Tuong Le, Minh Thanh Vo, Tung Kieu, Eenjun Hwang, Seungmin Rho, Sung Wook Baik

Research output: Contribution to journalArticlepeer-review

49 Citations (Scopus)


Electric energy consumption forecasting is an interesting, challenging, and important issue in energy management and equipment efficiency improvement. Existing approaches are predictive models that have the ability to predict for a specific profile, i.e., a time series of a whole building or an individual household in a smart building. In practice, there are many profiles in each smart building, which leads to time-consuming and expensive system resources. Therefore, this study develops a robust framework for the Multiple Electric Energy Consumption forecasting (MEC) of a smart building using Transfer Learning and Long Short-Term Memory (TLL), the so-called MEC-TLL framework. In this framework, we first employ a k-means clustering algorithm to cluster the daily load demand of many profiles in the training set. In this phase, we also perform Silhouette analysis to specify the optimal number of clusters for the experimental datasets. Next, this study develops the MEC training algorithm, which utilizes a cluster-based strategy for transfer learning the Long Short-Term Memory models to reduce the computational time. Finally, extensive experiments are conducted to compare the computational time and different performance metrics for multiple electric energy consumption forecasting on two smart buildings in South Korea. The experimental results indicate that our proposed approach is capable of economical overheads while achieving superior performances. Therefore, the proposed approach can be applied effectively for intelligent energy management in smart buildings.

Original languageEnglish
Article number2668
JournalSensors (Switzerland)
Issue number9
Publication statusPublished - 2020 May 1


  • Intelligent energy management system
  • Long short-term memory networks
  • Multiple electric energy consumption forecasting
  • The cluster-based strategy for transfer learning
  • Transfer learning

ASJC Scopus subject areas

  • Analytical Chemistry
  • Information Systems
  • Atomic and Molecular Physics, and Optics
  • Biochemistry
  • Instrumentation
  • Electrical and Electronic Engineering


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