Modeling and forecasting energy consumption for heterogeneous buildings using a physical -statistical approach

Xiaoshu Lu, Tao Lu, Charles J. Kilbert, Martti Viljanen

Research output: Contribution to journalArticleScientificpeer-review

125 Citations (Scopus)


Energy consumption forecasting is a critical and necessary input to planning and controlling energy usage in the building sector which accounts for 40% of the world’s energy use and the world’s greatest fraction of greenhouse gas emissions. However, due to the diversity and complexity of buildings as well as the random nature of weather conditions, energy consumption and loads are stochastic and difficult to predict. This paper presents a new methodology for energy demand forecasting that addresses the heterogeneity challenges in energy modeling of buildings. The new method is based on a physical–statistical approach designed to account for building heterogeneity to improve forecast accuracy. The physical model provides a theoretical input to characterize the underlying physical mechanism of energy flows. Then stochastic parameters are introduced into the physical model and the statistical time series model is formulated to reflect model uncertainties and individual heterogeneity in buildings. A new method of model generalization based on a convex hull technique is further derived to parameterize the individual-level model parameters for consistent model coefficients while maintaining satisfactory modeling accuracy for heterogeneous buildings. The proposed method and its validation are presented in detail for four different sports buildings with field measurements. The results show that the proposed methodology and model can provide a considerable improvement in forecasting accuracy.
Original languageEnglish
Pages (from-to)261–275
JournalApplied Energy
Issue number15
Publication statusPublished - 15 Apr 2015
MoE publication typeA1 Journal article-refereed


  • energy consumption
  • heterogeneous buildings
  • modeling method
  • physical-statistical approach


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