Tuning Machine Learning Models for Prediction of Building Energy Loads

Seyedzadeh, Saleh, Rahimian, Farzad Pour, Rastogi, Parag and Glesk, Ivan (2019) Tuning Machine Learning Models for Prediction of Building Energy Loads. Sustainable Cities and Society, 47. p. 101484. ISSN 2210-6707

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Official URL: https://doi.org/10.1016/j.scs.2019.101484


There have been numerous simulation tools utilised for calculating building energy loads for efficient design and retrofitting. However, these tools entail a great deal of computational cost and prior knowledge to work with. Machine Learning (ML) techniques can contribute to bridging this gap by taking advantage of existing historical data for forecasting new samples and lead to informed decisions. This study investigated the accuracy of most popular ML models in the prediction of buildings heating and cooling loads carrying out specific tuning for each ML model and using two simulated building energy data generated in EnergyPlus and Ecotect and compared the results. The study used a grid-search coupled with cross-validation method to examine the combinations of model parameters. Furthermore, sensitivity analysis techniques were 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. Comparing the accuracy of the tuned models with the original research works reveals the significant role of model optimisation. The outcomes of the sensitivity analysis are demonstrated as relative importance which resulted in the identification of unimportant variables and faster model fitting.

Item Type: Article
Uncontrolled Keywords: Building energy loads, Energy prediction, Machine learning, Energy modelling, Energy simulation, Building design
Subjects: G400 Computer Science
K200 Building
Department: Faculties > Engineering and Environment > Mechanical and Construction Engineering
Depositing User: Paul Burns
Date Deposited: 22 Feb 2019 09:48
Last Modified: 31 Jul 2021 19:02
URI: http://nrl.northumbria.ac.uk/id/eprint/38170

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