Machine learning models for prediction of building energy performance

Saleh Seyedzadeh, Farzad Pour Rahimian

Research output: Chapter in Book/Report/Conference proceedingChapter


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
PublisherSpringer Science and Business Media Deutschland GmbH
Number of pages22
ISBN (Electronic)9783030647513
ISBN (Print)9783030647506
Publication statusPublished - 16 Jan 2021

Publication series

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

Bibliographical note

Publisher Copyright:
© The Author(s), under exclusive license to Springer Nature Switzerland AG 2021.

Copyright 2021 Elsevier B.V., All rights reserved.


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