AI for Energy: Data-efficient and explainable algorithms to exploit energy flexibility
Year: 2025
Type: article
Abstract: Chris gave an invited talk on the Sustainable Electric Power Generation under EU Conditions workshop (https://kee.fei.tuke.sk/?page_id=2297), highlighting a number of AI techniques that will be used, improved and validated in BlueBird to make buildings truly flexible assets.The talk discussed:- Physics-informed reinforcement learning as a way to learn smart control policies from limited data sets by infusing a priori physics knowledge into neural networks.- Distillation of interpretable decision trees from black-box policies as a solution to improve the transparency of AI based control systems.- The HomeLabGym python library which offers a standardized interface to easily validate new control policies in HomeLab, a real house used as living lab environment.
Author: Develder, Chris
Institutions: Ghent University, IMEC
Language: English
Topic: Smart Grid Energy Management
Subfield: Electrical and Electronic Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: green
Funder: European Commission
OpenAlex ID: W7081944826
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