Recent Advances in Machine Learning for Building Envelopes: From Prediction to Optimization

Опубликована: Янв. 1, 2025

Nowadays, advanced building envelopes not only need to meet traditional design requirements but also address emerging demands, such as achieving low-carbon transition of buildings and mitigating the urban heat island (UHI) effect. Given intricacy indoor conditions complexity variables, approaches can hardly keep pace with evolving demands. Therefore, integrating Artificial Intelligence (AI) into envelope is trending in recent years. This paper provides a holistic review research on machine learning (ML) design. Popular ML algorithms, data input requirements, output generation are first elucidated, aiming shed light selection appropriate algorithms for specific datasets achieve optimal outcomes. ML-involved studies related types (e.g., building-integrated photovoltaic (BIPV), green roofs, PCM-integrated walls, glazing systems, etc.) discussed. The further highlights capabilities AI technologies predicting parameters material properties, environmental impact) optimizing criteria minimizing energy consumption), from micro-scope (i.e., microenvironment) macro-scope impact heat). work anticipated yield valuable insights promoting AI-driven solutions tackle both conventional challenges sustainable development.

Язык: Английский

A hybrid Gaussian process-integrated deep learning model for retrofitted building energy optimization in smart city ecosystems

Applied Energy, Год журнала: 2025, Номер 388, С. 125643 - 125643

Опубликована: Март 19, 2025

Язык: Английский