Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Eine Plattform für die Wissenschaft: Bauingenieurwesen, Architektur und Urbanistik
Building Energy Data-Driven Model Improved by Multi-objective Optimisation
https://orcid.org/https://orcid.org/0000-0001-6017-289X
https://orcid.org/https://orcid.org/0000-0001-7443-4723
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.
Building Energy Data-Driven Model Improved by Multi-objective Optimisation
https://orcid.org/https://orcid.org/0000-0001-6017-289X
https://orcid.org/https://orcid.org/0000-0001-7443-4723
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.
Building Energy Data-Driven Model Improved by Multi-objective Optimisation
https://orcid.org/https://orcid.org/0000-0001-6017-289X
https://orcid.org/https://orcid.org/0000-0001-7443-4723
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.
Building Energy Data-Driven Model Improved by Multi-objective Optimisation
Green Energy,Technology
Seyedzadeh, Saleh (Autor:in) / Pour Rahimian, Farzad (Autor:in)
Data-Driven Modelling of Non-Domestic Buildings Energy Performance ; Kapitel: 6 ; 99-109
16.01.2021
11 pages
Aufsatz/Kapitel (Buch)
Elektronische Ressource
Englisch
Data Driven Model Improved by Multi-Objective Optimisation for Prediction of Building Energy Loads
| BASE | 2020
| British Library Online Contents | 2018
| British Library Online Contents | 2018
Multi-objective Optimisation and Building Retrofit Planning
| Springer Verlag | 2021