Introduction remarks: SAFE-ENERGY
Clayton Miller
Safety of machine learning and data acquisition for urban analytics and GeoAI
Matias Quintana
On interpretable and explainable machine learning for urban building energy modeling and calibration
Yu Qian Ang, Nan Ma, Ming Yan
Explainable Home Energy Management Systems based on Reinforcement Learning using Differentiable Decision Trees
Garhya Gokhale, Bert Claessens, Chris Develder
Break
Data or Algorithms: Reliability and Interpretability of Machine Learning in Building Load Forecasting
Maomao Hu
Beyond empirical risk: testing data-driven energy models in sparse and biased data regimes
Hussain Kazmi, Attila Balint, Fuyang Jiang, Jilles De Blauwe, Fahad Mehmood
Panel discussion: On the trustworthiness and safety of machine learning models in regulated energy settings, and how to get there