---
_id: '60571'
abstract:
- lang: eng
text: As a possible solution to the demographic change and the resulting
knowledge loss due to retirements in the Energy sector, this study aimed to develop
a generic pipeline to implement and evaluate proof-of-concepts (PoCs) for LLM-based
assistance systems in new domains. Our pipeline contains an LLM-based data generation
strategy based on documents, a retrieval-augmented generation (RAG) architecture
utilizing prompting techniques on existing German LLMs, and an LLM-based automatic
evaluation strategy. We leverage our pipeline to evaluate five LLMs using data
from a German DSO. We found that the Llama3 and the Mistral model are appropriately
aligned for the task. We plan to pilot the RAG architecture in the DSO's infrastructure
for future research and continuously research improvements using the generated
human demonstrations.
author:
- first_name: Sascha
full_name: Kaltenpoth, Sascha
last_name: Kaltenpoth
- first_name: Oliver
full_name: Müller, Oliver
last_name: Müller
citation:
ama: Kaltenpoth S, Müller O. Don’t Touch the Power Line - A Proof-of-Concept for
Aligned LLM-Based Assistance Systems to Support the Maintenance in the Electricity
Distribution System. ACM SIGEnergy Energy Informatics Review. 2025;4(4):16-22.
doi:10.1145/3717413.3717415
apa: Kaltenpoth, S., & Müller, O. (2025). Don’t Touch the Power Line - A Proof-of-Concept
for Aligned LLM-Based Assistance Systems to Support the Maintenance in the Electricity
Distribution System. ACM SIGEnergy Energy Informatics Review, 4(4),
16–22. https://doi.org/10.1145/3717413.3717415
bibtex: '@article{Kaltenpoth_Müller_2025, title={Don’t Touch the Power Line - A
Proof-of-Concept for Aligned LLM-Based Assistance Systems to Support the Maintenance
in the Electricity Distribution System}, volume={4}, DOI={10.1145/3717413.3717415},
number={4}, journal={ACM SIGEnergy Energy Informatics Review}, publisher={Association
for Computing Machinery (ACM)}, author={Kaltenpoth, Sascha and Müller, Oliver},
year={2025}, pages={16–22} }'
chicago: 'Kaltenpoth, Sascha, and Oliver Müller. “Don’t Touch the Power Line - A
Proof-of-Concept for Aligned LLM-Based Assistance Systems to Support the Maintenance
in the Electricity Distribution System.” ACM SIGEnergy Energy Informatics Review
4, no. 4 (2025): 16–22. https://doi.org/10.1145/3717413.3717415.'
ieee: 'S. Kaltenpoth and O. Müller, “Don’t Touch the Power Line - A Proof-of-Concept
for Aligned LLM-Based Assistance Systems to Support the Maintenance in the Electricity
Distribution System,” ACM SIGEnergy Energy Informatics Review, vol. 4,
no. 4, pp. 16–22, 2025, doi: 10.1145/3717413.3717415.'
mla: Kaltenpoth, Sascha, and Oliver Müller. “Don’t Touch the Power Line - A Proof-of-Concept
for Aligned LLM-Based Assistance Systems to Support the Maintenance in the Electricity
Distribution System.” ACM SIGEnergy Energy Informatics Review, vol. 4,
no. 4, Association for Computing Machinery (ACM), 2025, pp. 16–22, doi:10.1145/3717413.3717415.
short: S. Kaltenpoth, O. Müller, ACM SIGEnergy Energy Informatics Review 4 (2025)
16–22.
date_created: 2025-07-09T09:51:31Z
date_updated: 2025-07-09T09:51:52Z
doi: 10.1145/3717413.3717415
intvolume: ' 4'
issue: '4'
language:
- iso: eng
page: 16-22
publication: ACM SIGEnergy Energy Informatics Review
publication_identifier:
issn:
- 2770-5331
publication_status: published
publisher: Association for Computing Machinery (ACM)
status: public
title: Don't Touch the Power Line - A Proof-of-Concept for Aligned LLM-Based Assistance
Systems to Support the Maintenance in the Electricity Distribution System
type: journal_article
user_id: '50640'
volume: 4
year: '2025'
...
---
_id: '61100'
abstract:
- lang: eng
text: As a possible solution to the demographic change and the resulting
knowledge loss due to retirements in the Energy sector, this study aimed to develop
a generic pipeline to implement and evaluate proof-of-concepts (PoCs) for LLM-based
assistance systems in new domains. Our pipeline contains an LLM-based data generation
strategy based on documents, a retrieval-augmented generation (RAG) architecture
utilizing prompting techniques on existing German LLMs, and an LLM-based automatic
evaluation strategy. We leverage our pipeline to evaluate five LLMs using data
from a German DSO. We found that the Llama3 and the Mistral model are appropriately
aligned for the task. We plan to pilot the RAG architecture in the DSO's infrastructure
for future research and continuously research improvements using the generated
human demonstrations.
author:
- first_name: Sascha
full_name: Kaltenpoth, Sascha
last_name: Kaltenpoth
- first_name: Oliver
full_name: Müller, Oliver
last_name: Müller
citation:
ama: Kaltenpoth S, Müller O. Don’t Touch the Power Line - A Proof-of-Concept for
Aligned LLM-Based Assistance Systems to Support the Maintenance in the Electricity
Distribution System. ACM SIGEnergy Energy Informatics Review. 2025;4(4):16-22.
doi:10.1145/3717413.3717415
apa: Kaltenpoth, S., & Müller, O. (2025). Don’t Touch the Power Line - A Proof-of-Concept
for Aligned LLM-Based Assistance Systems to Support the Maintenance in the Electricity
Distribution System. ACM SIGEnergy Energy Informatics Review, 4(4),
16–22. https://doi.org/10.1145/3717413.3717415
bibtex: '@article{Kaltenpoth_Müller_2025, title={Don’t Touch the Power Line - A
Proof-of-Concept for Aligned LLM-Based Assistance Systems to Support the Maintenance
in the Electricity Distribution System}, volume={4}, DOI={10.1145/3717413.3717415},
number={4}, journal={ACM SIGEnergy Energy Informatics Review}, publisher={Association
for Computing Machinery (ACM)}, author={Kaltenpoth, Sascha and Müller, Oliver},
year={2025}, pages={16–22} }'
chicago: 'Kaltenpoth, Sascha, and Oliver Müller. “Don’t Touch the Power Line - A
Proof-of-Concept for Aligned LLM-Based Assistance Systems to Support the Maintenance
in the Electricity Distribution System.” ACM SIGEnergy Energy Informatics Review
4, no. 4 (2025): 16–22. https://doi.org/10.1145/3717413.3717415.'
ieee: 'S. Kaltenpoth and O. Müller, “Don’t Touch the Power Line - A Proof-of-Concept
for Aligned LLM-Based Assistance Systems to Support the Maintenance in the Electricity
Distribution System,” ACM SIGEnergy Energy Informatics Review, vol. 4,
no. 4, pp. 16–22, 2025, doi: 10.1145/3717413.3717415.'
mla: Kaltenpoth, Sascha, and Oliver Müller. “Don’t Touch the Power Line - A Proof-of-Concept
for Aligned LLM-Based Assistance Systems to Support the Maintenance in the Electricity
Distribution System.” ACM SIGEnergy Energy Informatics Review, vol. 4,
no. 4, Association for Computing Machinery (ACM), 2025, pp. 16–22, doi:10.1145/3717413.3717415.
short: S. Kaltenpoth, O. Müller, ACM SIGEnergy Energy Informatics Review 4 (2025)
16–22.
date_created: 2025-08-30T14:35:37Z
date_updated: 2025-08-30T14:36:15Z
doi: 10.1145/3717413.3717415
intvolume: ' 4'
issue: '4'
language:
- iso: eng
page: 16-22
publication: ACM SIGEnergy Energy Informatics Review
publication_identifier:
issn:
- 2770-5331
publication_status: published
publisher: Association for Computing Machinery (ACM)
status: public
title: Don't Touch the Power Line - A Proof-of-Concept for Aligned LLM-Based Assistance
Systems to Support the Maintenance in the Electricity Distribution System
type: journal_article
user_id: '50640'
volume: 4
year: '2025'
...
---
_id: '61160'
abstract:
- lang: eng
text: As a possible solution to the demographic change and the resulting
knowledge loss due to retirements in the Energy sector, this study aimed to develop
a generic pipeline to implement and evaluate proof-of-concepts (PoCs) for LLM-based
assistance systems in new domains. Our pipeline contains an LLM-based data generation
strategy based on documents, a retrieval-augmented generation (RAG) architecture
utilizing prompting techniques on existing German LLMs, and an LLM-based automatic
evaluation strategy. We leverage our pipeline to evaluate five LLMs using data
from a German DSO. We found that the Llama3 and the Mistral model are appropriately
aligned for the task. We plan to pilot the RAG architecture in the DSO's infrastructure
for future research and continuously research improvements using the generated
human demonstrations.
article_type: original
author:
- first_name: Sascha Benjamin
full_name: Kaltenpoth, Sascha Benjamin
id: '50640'
last_name: Kaltenpoth
- first_name: Oliver
full_name: Müller, Oliver
id: '72849'
last_name: Müller
citation:
ama: Kaltenpoth SB, Müller O. Don’t Touch the Power Line - A Proof-of-Concept for
Aligned LLM-Based Assistance Systems to Support the Maintenance in the Electricity
Distribution System. ACM SIGEnergy Energy Informatics Review. 2025;4(4):16-22.
doi:10.1145/3717413.3717415
apa: Kaltenpoth, S. B., & Müller, O. (2025). Don’t Touch the Power Line - A
Proof-of-Concept for Aligned LLM-Based Assistance Systems to Support the Maintenance
in the Electricity Distribution System. ACM SIGEnergy Energy Informatics Review,
4(4), 16–22. https://doi.org/10.1145/3717413.3717415
bibtex: '@article{Kaltenpoth_Müller_2025, title={Don’t Touch the Power Line - A
Proof-of-Concept for Aligned LLM-Based Assistance Systems to Support the Maintenance
in the Electricity Distribution System}, volume={4}, DOI={10.1145/3717413.3717415},
number={4}, journal={ACM SIGEnergy Energy Informatics Review}, publisher={Association
for Computing Machinery (ACM)}, author={Kaltenpoth, Sascha Benjamin and Müller,
Oliver}, year={2025}, pages={16–22} }'
chicago: 'Kaltenpoth, Sascha Benjamin, and Oliver Müller. “Don’t Touch the Power
Line - A Proof-of-Concept for Aligned LLM-Based Assistance Systems to Support
the Maintenance in the Electricity Distribution System.” ACM SIGEnergy Energy
Informatics Review 4, no. 4 (2025): 16–22. https://doi.org/10.1145/3717413.3717415.'
ieee: 'S. B. Kaltenpoth and O. Müller, “Don’t Touch the Power Line - A Proof-of-Concept
for Aligned LLM-Based Assistance Systems to Support the Maintenance in the Electricity
Distribution System,” ACM SIGEnergy Energy Informatics Review, vol. 4,
no. 4, pp. 16–22, 2025, doi: 10.1145/3717413.3717415.'
mla: Kaltenpoth, Sascha Benjamin, and Oliver Müller. “Don’t Touch the Power Line
- A Proof-of-Concept for Aligned LLM-Based Assistance Systems to Support the Maintenance
in the Electricity Distribution System.” ACM SIGEnergy Energy Informatics Review,
vol. 4, no. 4, Association for Computing Machinery (ACM), 2025, pp. 16–22, doi:10.1145/3717413.3717415.
short: S.B. Kaltenpoth, O. Müller, ACM SIGEnergy Energy Informatics Review 4 (2025)
16–22.
date_created: 2025-09-09T12:30:30Z
date_updated: 2025-09-09T12:31:07Z
doi: 10.1145/3717413.3717415
intvolume: ' 4'
issue: '4'
language:
- iso: eng
page: 16-22
publication: ACM SIGEnergy Energy Informatics Review
publication_identifier:
issn:
- 2770-5331
publication_status: published
publisher: Association for Computing Machinery (ACM)
status: public
title: Don't Touch the Power Line - A Proof-of-Concept for Aligned LLM-Based Assistance
Systems to Support the Maintenance in the Electricity Distribution System
type: journal_article
user_id: '50640'
volume: 4
year: '2025'
...
---
_id: '63397'
abstract:
- lang: eng
text: Decarbonizing industrial process heat is a crucial step in mitigating climate
change. While Process Mining (PM) has gained traction in sustainability research—such
as optimizing production scheduling to reduce energy use or accounting for carbon
footprints—it has largely overlooked the challenges and opportunities related
to thermal energy, accounting for 66% of total energy demand in industrial processes.
At the same time, Heat Integration (HI) is an established engineering discipline
focused on maximizing the efficiency of thermal energy systems. However, HI traditionally
relies on static or incomplete data about energy demands, limiting its effectiveness
and accuracy. In this paper, we propose a novel framework that combines PM and
HI to enable data-driven, process- and product-centric modeling of industrial
energy demands. By integrating event logs and thermal energy data, our approach
allows for a fine-grained analysis of heat demand patterns corresponding to specific
process activities and product variants. We demonstrate the applicability and
advantages of the framework by simulating a pharmaceutical manufacturing process
and evaluating energy demands and heat recovery potentials. Our findings show
that our PM-enabled HI framework provides more accurate and actionable insights
into the temporal and product-specific variation of thermal energy demands. By
capturing the causal relationships between process activities, product characteristics,
and energy consumption, our approach enables improved analysis, planning, and
optimization for heat recovery and process decarbonization. This integration of
PM and HI expands the analytical tools for both disciplines and contributes to
advancing the sustainable transformation of industrial processes.
author:
- first_name: David Ricardo
full_name: Zapata Gonzalez, David Ricardo
id: '105506'
last_name: Zapata Gonzalez
- first_name: Katharina
full_name: Brennig, Katharina
id: '51905'
last_name: Brennig
- first_name: Kay
full_name: Benkert, Kay
last_name: Benkert
- first_name: Florian
full_name: Schlosser, Florian
id: '88614'
last_name: Schlosser
- first_name: Oliver
full_name: Müller, Oliver
id: '72849'
last_name: Müller
citation:
ama: 'Zapata Gonzalez DR, Brennig K, Benkert K, Schlosser F, Müller O. Process Mining
for Robust Heat Integration through Process- and Product-Centric Energy Demand
Modeling. In: ACM SIGEnergy Energy Informatics Review. Vol 5. Association
for Computing Machinery (ACM); 2025:19-31. doi:10.1145/3777518.3777520'
apa: Zapata Gonzalez, D. R., Brennig, K., Benkert, K., Schlosser, F., & Müller,
O. (2025). Process Mining for Robust Heat Integration through Process- and Product-Centric
Energy Demand Modeling. ACM SIGEnergy Energy Informatics Review, 5(3),
19–31. https://doi.org/10.1145/3777518.3777520
bibtex: '@inproceedings{Zapata Gonzalez_Brennig_Benkert_Schlosser_Müller_2025, title={Process
Mining for Robust Heat Integration through Process- and Product-Centric Energy
Demand Modeling}, volume={5}, DOI={10.1145/3777518.3777520},
number={3}, booktitle={ACM SIGEnergy Energy Informatics Review}, publisher={Association
for Computing Machinery (ACM)}, author={Zapata Gonzalez, David Ricardo and Brennig,
Katharina and Benkert, Kay and Schlosser, Florian and Müller, Oliver}, year={2025},
pages={19–31} }'
chicago: Zapata Gonzalez, David Ricardo, Katharina Brennig, Kay Benkert, Florian
Schlosser, and Oliver Müller. “Process Mining for Robust Heat Integration through
Process- and Product-Centric Energy Demand Modeling.” In ACM SIGEnergy Energy
Informatics Review, 5:19–31. Association for Computing Machinery (ACM), 2025.
https://doi.org/10.1145/3777518.3777520.
ieee: 'D. R. Zapata Gonzalez, K. Brennig, K. Benkert, F. Schlosser, and O. Müller,
“Process Mining for Robust Heat Integration through Process- and Product-Centric
Energy Demand Modeling,” in ACM SIGEnergy Energy Informatics Review, 2025,
vol. 5, no. 3, pp. 19–31, doi: 10.1145/3777518.3777520.'
mla: Zapata Gonzalez, David Ricardo, et al. “Process Mining for Robust Heat Integration
through Process- and Product-Centric Energy Demand Modeling.” ACM SIGEnergy
Energy Informatics Review, vol. 5, no. 3, Association for Computing Machinery
(ACM), 2025, pp. 19–31, doi:10.1145/3777518.3777520.
short: 'D.R. Zapata Gonzalez, K. Brennig, K. Benkert, F. Schlosser, O. Müller, in:
ACM SIGEnergy Energy Informatics Review, Association for Computing Machinery (ACM),
2025, pp. 19–31.'
date_created: 2025-12-22T13:13:37Z
date_updated: 2025-12-22T13:15:08Z
doi: 10.1145/3777518.3777520
intvolume: ' 5'
issue: '3'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://dl.acm.org/doi/abs/10.1145/3777518.3777520
oa: '1'
page: 19-31
publication: ACM SIGEnergy Energy Informatics Review
publication_identifier:
issn:
- 2770-5331
- 2770-5331
publication_status: published
publisher: Association for Computing Machinery (ACM)
status: public
title: Process Mining for Robust Heat Integration through Process- and Product-Centric
Energy Demand Modeling
type: conference
user_id: '105506'
volume: 5
year: '2025'
...
---
_id: '63400'
abstract:
- lang: eng
text: Data centers (DCs) form the backbone of our growing digital economy, but their
rising energy demands pose challenges to our environment. At the same time, reusing
waste heat from DCs also represents an opportunity, for example, for more sustainable
heating of residential buildings. Modeling and optimizing these coupled and dynamic
systems of heat generation and reuse is complex. On the one hand, physical simulations
can be used to model these systems, but they are time-consuming to develop and
run. Machine learning (ML), on the other hand, allows efficient data-driven modeling,
but conventional correlation-based approaches struggle with the prediction of
interventions and out-of-distribution generalization. Recent advances in causal
ML, which combine principles from causal inference with flexible ML methods, are
a promising approach for more robust predictions. Due to their focus on modeling
interventions and cause-and-effect relationships, it is difficult to evaluate
causal ML approaches rigorously. To address this challenge, we built a testbed
of a miniature DC with an integrated waste heat network, equipped with sensors
and actuators. This testbed allows conducting controlled experiments and automatic
collection of realistic data, which can then be used to benchmark conventional
and causal ML methods. Our experimental results highlight the strengths and weaknesses
of each modeling approach, providing valuable insights on how to appropriately
apply different types of machine learning to optimize data center operations and
enhance their sustainability.
author:
- first_name: David Ricardo
full_name: Zapata Gonzalez, David Ricardo
id: '105506'
last_name: Zapata Gonzalez
- first_name: Marcel
full_name: Meyer, Marcel
id: '105120'
last_name: Meyer
- first_name: Oliver
full_name: Müller, Oliver
id: '72849'
last_name: Müller
citation:
ama: 'Zapata Gonzalez DR, Meyer M, Müller O. Causal Machine Learning Approaches
for Modelling Data Center Heat Recovery: A Physical Testbed Study. In: ACM
SIGEnergy Energy Informatics Review. Vol 5. Association for Computing Machinery
(ACM); 2025:4-10. doi:10.1145/3757892.3757893'
apa: 'Zapata Gonzalez, D. R., Meyer, M., & Müller, O. (2025). Causal Machine
Learning Approaches for Modelling Data Center Heat Recovery: A Physical Testbed
Study. ACM SIGEnergy Energy Informatics Review, 5(2), 4–10. https://doi.org/10.1145/3757892.3757893'
bibtex: '@inproceedings{Zapata Gonzalez_Meyer_Müller_2025, title={Causal Machine
Learning Approaches for Modelling Data Center Heat Recovery: A Physical Testbed
Study}, volume={5}, DOI={10.1145/3757892.3757893},
number={2}, booktitle={ACM SIGEnergy Energy Informatics Review}, publisher={Association
for Computing Machinery (ACM)}, author={Zapata Gonzalez, David Ricardo and Meyer,
Marcel and Müller, Oliver}, year={2025}, pages={4–10} }'
chicago: 'Zapata Gonzalez, David Ricardo, Marcel Meyer, and Oliver Müller. “Causal
Machine Learning Approaches for Modelling Data Center Heat Recovery: A Physical
Testbed Study.” In ACM SIGEnergy Energy Informatics Review, 5:4–10. Association
for Computing Machinery (ACM), 2025. https://doi.org/10.1145/3757892.3757893.'
ieee: 'D. R. Zapata Gonzalez, M. Meyer, and O. Müller, “Causal Machine Learning
Approaches for Modelling Data Center Heat Recovery: A Physical Testbed Study,”
in ACM SIGEnergy Energy Informatics Review, 2025, vol. 5, no. 2, pp. 4–10,
doi: 10.1145/3757892.3757893.'
mla: 'Zapata Gonzalez, David Ricardo, et al. “Causal Machine Learning Approaches
for Modelling Data Center Heat Recovery: A Physical Testbed Study.” ACM SIGEnergy
Energy Informatics Review, vol. 5, no. 2, Association for Computing Machinery
(ACM), 2025, pp. 4–10, doi:10.1145/3757892.3757893.'
short: 'D.R. Zapata Gonzalez, M. Meyer, O. Müller, in: ACM SIGEnergy Energy Informatics
Review, Association for Computing Machinery (ACM), 2025, pp. 4–10.'
date_created: 2025-12-22T13:19:06Z
date_updated: 2025-12-22T13:20:02Z
doi: 10.1145/3757892.3757893
intvolume: ' 5'
issue: '2'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://dl.acm.org/doi/abs/10.1145/3757892.3757893
oa: '1'
page: 4-10
publication: ACM SIGEnergy Energy Informatics Review
publication_identifier:
issn:
- 2770-5331
- 2770-5331
publication_status: published
publisher: Association for Computing Machinery (ACM)
status: public
title: 'Causal Machine Learning Approaches for Modelling Data Center Heat Recovery:
A Physical Testbed Study'
type: conference
user_id: '105506'
volume: 5
year: '2025'
...
---
_id: '63399'
abstract:
- lang: eng
text: Data centers (DCs) form the backbone of our growing digital economy, but their
rising energy demands pose challenges to our environment. At the same time, reusing
waste heat from DCs also represents an opportunity, for example, for more sustainable
heating of residential buildings. Modeling and optimizing these coupled and dynamic
systems of heat generation and reuse is complex. On the one hand, physical simulations
can be used to model these systems, but they are time-consuming to develop and
run. Machine learning (ML), on the other hand, allows efficient data-driven modeling,
but conventional correlation-based approaches struggle with the prediction of
interventions and out-of-distribution generalization. Recent advances in causal
ML, which combine principles from causal inference with flexible ML methods, are
a promising approach for more robust predictions. Due to their focus on modeling
interventions and cause-and-effect relationships, it is difficult to evaluate
causal ML approaches rigorously. To address this challenge, we built a testbed
of a miniature DC with an integrated waste heat network, equipped with sensors
and actuators. This testbed allows conducting controlled experiments and automatic
collection of realistic data, which can then be used to benchmark conventional
and causal ML methods. Our experimental results highlight the strengths and weaknesses
of each modeling approach, providing valuable insights on how to appropriately
apply different types of machine learning to optimize data center operations and
enhance their sustainability.
author:
- first_name: David Zapata
full_name: Gonzalez, David Zapata
last_name: Gonzalez
- first_name: Marcel
full_name: Meyer, Marcel
last_name: Meyer
- first_name: Oliver
full_name: Müller, Oliver
last_name: Müller
citation:
ama: 'Gonzalez DZ, Meyer M, Müller O. Causal Machine Learning Approaches for Modelling
Data Center Heat Recovery: A Physical Testbed Study. In: ACM SIGEnergy Energy
Informatics Review. Vol 5. Association for Computing Machinery (ACM); 2025:4-10.
doi:10.1145/3757892.3757893'
apa: 'Gonzalez, D. Z., Meyer, M., & Müller, O. (2025). Causal Machine Learning
Approaches for Modelling Data Center Heat Recovery: A Physical Testbed Study.
ACM SIGEnergy Energy Informatics Review, 5(2), 4–10. https://doi.org/10.1145/3757892.3757893'
bibtex: '@inproceedings{Gonzalez_Meyer_Müller_2025, title={Causal Machine Learning
Approaches for Modelling Data Center Heat Recovery: A Physical Testbed Study},
volume={5}, DOI={10.1145/3757892.3757893},
number={2}, booktitle={ACM SIGEnergy Energy Informatics Review}, publisher={Association
for Computing Machinery (ACM)}, author={Gonzalez, David Zapata and Meyer, Marcel
and Müller, Oliver}, year={2025}, pages={4–10} }'
chicago: 'Gonzalez, David Zapata, Marcel Meyer, and Oliver Müller. “Causal Machine
Learning Approaches for Modelling Data Center Heat Recovery: A Physical Testbed
Study.” In ACM SIGEnergy Energy Informatics Review, 5:4–10. Association
for Computing Machinery (ACM), 2025. https://doi.org/10.1145/3757892.3757893.'
ieee: 'D. Z. Gonzalez, M. Meyer, and O. Müller, “Causal Machine Learning Approaches
for Modelling Data Center Heat Recovery: A Physical Testbed Study,” in ACM
SIGEnergy Energy Informatics Review, 2025, vol. 5, no. 2, pp. 4–10, doi: 10.1145/3757892.3757893.'
mla: 'Gonzalez, David Zapata, et al. “Causal Machine Learning Approaches for Modelling
Data Center Heat Recovery: A Physical Testbed Study.” ACM SIGEnergy Energy
Informatics Review, vol. 5, no. 2, Association for Computing Machinery (ACM),
2025, pp. 4–10, doi:10.1145/3757892.3757893.'
short: 'D.Z. Gonzalez, M. Meyer, O. Müller, in: ACM SIGEnergy Energy Informatics
Review, Association for Computing Machinery (ACM), 2025, pp. 4–10.'
date_created: 2025-12-22T13:17:36Z
date_updated: 2025-12-22T13:18:19Z
doi: 10.1145/3757892.3757893
intvolume: ' 5'
issue: '2'
language:
- iso: eng
main_file_link:
- open_access: '1'
url: https://dl.acm.org/doi/abs/10.1145/3757892.3757893
oa: '1'
page: 4-10
publication: ACM SIGEnergy Energy Informatics Review
publication_identifier:
issn:
- 2770-5331
- 2770-5331
publication_status: published
publisher: Association for Computing Machinery (ACM)
status: public
title: 'Causal Machine Learning Approaches for Modelling Data Center Heat Recovery:
A Physical Testbed Study'
type: conference
user_id: '105506'
volume: 5
year: '2025'
...