| A Conditional Generative adversarial Network for energy use in multiple buildings using scarce data G Baasch, G Rousseau, R Evins Energy and AI 5, 100087, 2021 | 27 | 2021 |
| Comparing gray box methods to derive building properties from smart thermostat data G Baasch, A Wicikowski, G Faure, R Evins Proceedings of the 6th ACM international conference on systems for energy …, 2019 | 22 | 2019 |
| On the joint control of multiple building systems with reinforcement learning T Zhang, G Baasch, O Ardakanian, R Evins Proceedings of the Twelfth ACM International Conference on Future Energy …, 2021 | 17 | 2021 |
| Identifying whole-building heat loss coefficient from heterogeneous sensor data: An empirical survey of gray and black box approaches G Baasch, P Westermann, R Evins Energy and Buildings 241, 110889, 2021 | 15 | 2021 |
| BESOS: A collaborative building and energy simulation platform G Faure, T Christiaanse, R Evins, GM Baasch Proceedings of the 6th ACM International Conference on Systems for Energy …, 2019 | 14 | 2019 |
| Targeting Buildings for Energy Retrofit Using Recurrent Neural Networks with Multivariate Time Series GM Baasch, R Evins Neural Inf. Process. Syst 2019, 2019 | 7 | 2019 |
| Identification of thermal building properties using gray box and deep learning methods G Baasch | 1 | 2020 |
| Demo Abstract: BESOS-a Collaborative Building and Energy Simulation Platform G Faure, T Christiaanse, R Evins, GM Baasch | | |
