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.
|Title of host publication||Data-Driven Modelling of Non-Domestic Buildings Energy Performance|
|Publisher||Springer Science and Business Media Deutschland GmbH|
|Number of pages||22|
|Publication status||Published - 16 Jan 2021|
|Name||Green Energy and Technology|
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