Explainable Home Energy Management Systems based on Reinforcement Learning using Differentiable Decision Trees-Reference-Cited by-同舟云学术

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Explainable Home Energy Management Systems based on Reinforcement Learning using Differentiable Decision Trees

Published:2024-05-31 Issue: Volume: Page:687-691
ISSN:
Container-title:The 15th ACM International Conference on Future and Sustainable Energy Systems
language:
Short-container-title:

Author:

Gokhale Gargya¹^ORCID,Claessens Bert²^ORCID,Develder Chris¹^ORCID

Affiliation:

1. IDLab, Ghent University -imec, Belgium

2. beebop.ai, Belgium

Funder

Horizon 2020 Framework Programme

Energy Transition Fund (FOD Economy), Belgium

Publisher

ACM

Link

https://dl.acm.org/doi/pdf/10.1145/3632775.3663310

Reference25 articles.

1. AlphaESS. 2022. AlphaESS User Manual. Retrieved April 02, 2024 from https://www.alphaess.com/En/Skippower/downloadFile?id=129&mid=80

2. Reinforcement Learning for Selective Key Applications in Power Systems: Recent Advances and Future Challenges

3. All you need to know about model predictive control for buildings

4. Nicholas Frosst and Geoffrey Hinton. 2017. Distilling a neural network into a soft decision tree. arXiv preprint arXiv:1711.09784 (2017).

5. MILP-based model predictive control for home energy management systems: A real case study in Algarve, Portugal

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