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6 Publications
2025 | Journal Article | LibreCat-ID: 60571
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.
LibreCat
| DOI
2025 | Journal Article | LibreCat-ID: 61100
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.
LibreCat
| DOI
2025 | Journal Article | LibreCat-ID: 61160
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.
LibreCat
| DOI
2025 | Conference Paper | LibreCat-ID: 63397 | 
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.
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2025 | Conference Paper | LibreCat-ID: 63400 |
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.
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2025 | Conference Paper | LibreCat-ID: 63399 |
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.
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