This chapter proposes a method for optimising ML models for forecasting both heating and cooling loads. The technique employs multi-objective optimisation with evolutionary algorithms to search the space of possible parameters. The proposed approach not only tunes single model to precisely predict building energy loads but also accelerates the process of model optimisation. The chapter utilises simulated building energy data to validate the proposed method, and compares the outcomes with the regular ML tuning procedure (i.e. grid search). The optimised model provides a reliable tool for building designers and engineers to explore a large space of the available building materials and technologies.
|Title of host publication||Data-Driven Modelling of Non-Domestic Buildings Energy Performance|
|Publisher||Springer Science and Business Media Deutschland GmbH|
|Number of pages||11|
|Publication status||Published - 2021|
|Name||Green Energy and Technology|
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