Machine learning (ML) has been recognised as a powerful method for modelling building energy consumption. The capability of ML to provide a fast and accurate prediction of energy loads makes it an ideal tool for decision-making tasks related to sustainable design and retrofit planning. However, the accuracy of these ML models is much dependant on the selection of the right hyper-parameters for specific building dataset. This paper proposes a method for optimising ML model 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 tune one model to precisely predict building energy loads but also accelerates the process of model optimisation. The study utilises a simulated building energy data generated in EnergyPlus to demonstrate the efficiency of 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
|Construction and Management in Architecture, Engineering, Construction and Operations (AECO)
|Subtitle of host publication
|36th CIB W78 2019 Conference ICT in Design
|University of Northumbria, Newcastle-upon-Tyne.
|Number of pages
|Published - 18 Sept 2019
|36th CIB W78 2019 Conference: ICT in Design, Construction and Management in Architecture, Engineering, Construction and Operations (AECO) - Newcastle, United Kingdom
Duration: 18 Sept 2019 → 20 Sept 2019
|36th CIB W78 2019 Conference
|18/09/19 → 20/09/19