Applying Machine Learning to Develop Black-box Control Model of Active Double-Skin Facade

Abstract

The efficient energy performance of an active double-skin facade (DSF) has raised more attention to study and apply for developing the building control strategies and systems. Although DSFs can be actively operated in dynamic modes with controllable components such as shading slat angle, airflow path, and airflow rate, no autonomous control model has been deployed to utilize their maximum potential for building energy efficiency.

This thesis aims to apply Machine Learning (ML) to train predictive models for developing a black-box control model that estimates the deliverable operational modes of DSF for the desired energy performance parameters under the related environmental conditions. An autonomous control system of DSF to be developed based on applied ML algorithms as an advanced building control strategy is challenging to carry out for the first time. Data acquisition (DAQ) is also to be planned for future works.

The steady-state and dynamic simulations of a specific configuration of DSF were set up in EnergyPlus and used as training data processed in Python scripts. The simulations conducted thermal and visual performance against the possible variations of realistic boundary conditions and operational modes of DSF. After data analysis and identifying the ML problems, ANN, RF/ET, XGB predictive models were to learn for visual metric, airflow mode, and thermal metric in different airflow modes. Eventually, the black-box models were compared and selected according to several defined criteria of reliability. The DSF controller was designed by combining the selected models to control DSF operational modes and predict corresponding visual and thermal metrics. Python-programmed ML software libraries used are Scikit-learn, Keras based Tensorflow backend, and XGBoost.

In conclusion, the ML black-box models probably suffer from overfitting and instability due to noises in the real world. The proposed solution to reduce variance is to enlarge training data and retrain the black-box models by online transfer learning. Otherwise, it is still highly recommended to proceed with the reinforcement learning approach or hybrid models promising to overcome the limitations of the black-box models.