Forecasting district-scale energy dynamics through integrating building network and long short-term memory learning algorithm

Research output: Journal Publications and Reviews (RGC: 21, 22, 62)21_Publication in refereed journal

11 Scopus Citations
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Author(s)

  • Wei Wang
  • Tianzhen Hong
  • Xiaodong Xu
  • Ziang Liu
  • Ning Xu

Detail(s)

Original languageEnglish
Pages (from-to)217-230
Journal / PublicationApplied Energy
Volume248
Online published24 Apr 2019
Publication statusPublished - 15 Aug 2019

Abstract

With the development of data-driven techniques, district-scale building energy prediction has attracted increasing attention in recent years for revealing energy use patterns and reduction potentials. However, data acquisition in large building groups is difficult and adjacent buildings also interact with each other. To reduce data cost and incorporate the inter-building impact with the data-driven building energy model, this study proposes a deep learning predictive approach that fuses the building network model with a long short-term memory learning model for district-scale building energy modeling. The building network was constructed based on correlations between the energy use intensity of buildings, which can significantly reduce the computational complexity of the deep learning models for energy dynamic prediction. Five typical building groups with energy use data from 2015 to 2018 on two institutional campuses were selected to perform the validation experiment with TensorFlow. Based on the prediction error assessments, the results suggest that for total building energy use intensity prediction, the proposed model can achieve a mean absolute percentage error of 6.66% and a root mean square error of 0.36 kWh/m2, compared to 12.05% and 0.63 kWh/m2 of the conventional artificial neural network model and to 11.06% and 0.89 kWh/m2 for the support vector regression model.

Research Area(s)

  • Building network, Data-driven prediction, District-scale building energy modeling, Long short-term memory networks