Machine learning models for prediction of building energy performance

Saleh Seyedzadeh, Farzad Pour Rahimian

Research output: Chapter in Book/Report/Conference proceedingChapter

1 Citation (Scopus)


This chapter investigates the accuracy of most popular ML models in the prediction of building heating and cooling loads carrying out specific tuning for each ML model and using two simulated building energy data. The use of grid search coupled with cross-validation method in examination of the model parameters is demonstrated. Furthermore, sensitivity analysis techniques are used to evaluate the importance of input variables on the performance of ML models. The accuracy and time complexity of models in predicting heating and cooling loads are demonstrated.

Original languageEnglish
Title of host publicationData-Driven Modelling of Non-Domestic Buildings Energy Performance
Subtitle of host publicationSupporting Building Retrofit Planning
Place of PublicationCham, Switzerland
Number of pages22
ISBN (Print)978-3-030-64750-6
Publication statusPublished - 16 Jan 2021

Publication series

NameGreen Energy and Technology
ISSN (Print)1865-3529
ISSN (Electronic)1865-3537


  • building energy analysis
  • building energy performance
  • machine learing models


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