[{"type":"journal_article","publication":"ACM SIGEnergy Energy Informatics Review","status":"public","abstract":[{"text":"<jats:p>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.</jats:p>","lang":"eng"}],"user_id":"50640","_id":"60571","language":[{"iso":"eng"}],"issue":"4","publication_status":"published","publication_identifier":{"issn":["2770-5331"]},"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. <i>ACM SIGEnergy Energy Informatics Review</i>. 2025;4(4):16-22. doi:<a href=\"https://doi.org/10.1145/3717413.3717415\">10.1145/3717413.3717415</a>","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,” <i>ACM SIGEnergy Energy Informatics Review</i>, vol. 4, no. 4, pp. 16–22, 2025, doi: <a href=\"https://doi.org/10.1145/3717413.3717415\">10.1145/3717413.3717415</a>.","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.” <i>ACM SIGEnergy Energy Informatics Review</i> 4, no. 4 (2025): 16–22. <a href=\"https://doi.org/10.1145/3717413.3717415\">https://doi.org/10.1145/3717413.3717415</a>.","short":"S. Kaltenpoth, O. Müller, ACM SIGEnergy Energy Informatics Review 4 (2025) 16–22.","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={<a href=\"https://doi.org/10.1145/3717413.3717415\">10.1145/3717413.3717415</a>}, 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} }","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.” <i>ACM SIGEnergy Energy Informatics Review</i>, vol. 4, no. 4, Association for Computing Machinery (ACM), 2025, pp. 16–22, doi:<a href=\"https://doi.org/10.1145/3717413.3717415\">10.1145/3717413.3717415</a>.","apa":"Kaltenpoth, S., &#38; 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. <i>ACM SIGEnergy Energy Informatics Review</i>, <i>4</i>(4), 16–22. <a href=\"https://doi.org/10.1145/3717413.3717415\">https://doi.org/10.1145/3717413.3717415</a>"},"intvolume":"         4","page":"16-22","year":"2025","author":[{"first_name":"Sascha","last_name":"Kaltenpoth","full_name":"Kaltenpoth, Sascha"},{"full_name":"Müller, Oliver","last_name":"Müller","first_name":"Oliver"}],"date_created":"2025-07-09T09:51:31Z","volume":4,"date_updated":"2025-07-09T09:51:52Z","publisher":"Association for Computing Machinery (ACM)","doi":"10.1145/3717413.3717415","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"},{"year":"2025","intvolume":"         4","page":"16-22","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. <i>ACM SIGEnergy Energy Informatics Review</i>. 2025;4(4):16-22. doi:<a href=\"https://doi.org/10.1145/3717413.3717415\">10.1145/3717413.3717415</a>","apa":"Kaltenpoth, S., &#38; 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. <i>ACM SIGEnergy Energy Informatics Review</i>, <i>4</i>(4), 16–22. <a href=\"https://doi.org/10.1145/3717413.3717415\">https://doi.org/10.1145/3717413.3717415</a>","short":"S. Kaltenpoth, O. Müller, ACM SIGEnergy Energy Informatics Review 4 (2025) 16–22.","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={<a href=\"https://doi.org/10.1145/3717413.3717415\">10.1145/3717413.3717415</a>}, 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} }","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.” <i>ACM SIGEnergy Energy Informatics Review</i>, vol. 4, no. 4, Association for Computing Machinery (ACM), 2025, pp. 16–22, doi:<a href=\"https://doi.org/10.1145/3717413.3717415\">10.1145/3717413.3717415</a>.","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.” <i>ACM SIGEnergy Energy Informatics Review</i> 4, no. 4 (2025): 16–22. <a href=\"https://doi.org/10.1145/3717413.3717415\">https://doi.org/10.1145/3717413.3717415</a>.","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,” <i>ACM SIGEnergy Energy Informatics Review</i>, vol. 4, no. 4, pp. 16–22, 2025, doi: <a href=\"https://doi.org/10.1145/3717413.3717415\">10.1145/3717413.3717415</a>."},"publication_identifier":{"issn":["2770-5331"]},"publication_status":"published","issue":"4","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","doi":"10.1145/3717413.3717415","publisher":"Association for Computing Machinery (ACM)","date_updated":"2025-08-30T14:36:15Z","volume":4,"author":[{"last_name":"Kaltenpoth","full_name":"Kaltenpoth, Sascha","first_name":"Sascha"},{"first_name":"Oliver","full_name":"Müller, Oliver","last_name":"Müller"}],"date_created":"2025-08-30T14:35:37Z","abstract":[{"text":"<jats:p>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.</jats:p>","lang":"eng"}],"status":"public","publication":"ACM SIGEnergy Energy Informatics Review","type":"journal_article","language":[{"iso":"eng"}],"_id":"61100","user_id":"50640"},{"publisher":"Association for Computing Machinery (ACM)","date_updated":"2025-09-09T12:31:07Z","volume":4,"date_created":"2025-09-09T12:30:30Z","author":[{"full_name":"Kaltenpoth, Sascha Benjamin","id":"50640","last_name":"Kaltenpoth","first_name":"Sascha Benjamin"},{"id":"72849","full_name":"Müller, Oliver","last_name":"Müller","first_name":"Oliver"}],"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","doi":"10.1145/3717413.3717415","publication_identifier":{"issn":["2770-5331"]},"publication_status":"published","issue":"4","year":"2025","intvolume":"         4","page":"16-22","citation":{"apa":"Kaltenpoth, S. B., &#38; 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. <i>ACM SIGEnergy Energy Informatics Review</i>, <i>4</i>(4), 16–22. <a href=\"https://doi.org/10.1145/3717413.3717415\">https://doi.org/10.1145/3717413.3717415</a>","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={<a href=\"https://doi.org/10.1145/3717413.3717415\">10.1145/3717413.3717415</a>}, 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} }","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.” <i>ACM SIGEnergy Energy Informatics Review</i>, vol. 4, no. 4, Association for Computing Machinery (ACM), 2025, pp. 16–22, doi:<a href=\"https://doi.org/10.1145/3717413.3717415\">10.1145/3717413.3717415</a>.","short":"S.B. Kaltenpoth, O. Müller, ACM SIGEnergy Energy Informatics Review 4 (2025) 16–22.","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. <i>ACM SIGEnergy Energy Informatics Review</i>. 2025;4(4):16-22. doi:<a href=\"https://doi.org/10.1145/3717413.3717415\">10.1145/3717413.3717415</a>","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,” <i>ACM SIGEnergy Energy Informatics Review</i>, vol. 4, no. 4, pp. 16–22, 2025, doi: <a href=\"https://doi.org/10.1145/3717413.3717415\">10.1145/3717413.3717415</a>.","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.” <i>ACM SIGEnergy Energy Informatics Review</i> 4, no. 4 (2025): 16–22. <a href=\"https://doi.org/10.1145/3717413.3717415\">https://doi.org/10.1145/3717413.3717415</a>."},"_id":"61160","user_id":"50640","article_type":"original","language":[{"iso":"eng"}],"publication":"ACM SIGEnergy Energy Informatics Review","type":"journal_article","abstract":[{"text":"<jats:p>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.</jats:p>","lang":"eng"}],"status":"public"},{"date_updated":"2025-12-22T13:15:08Z","oa":"1","publisher":"Association for Computing Machinery (ACM)","volume":5,"author":[{"first_name":"David Ricardo","id":"105506","full_name":"Zapata Gonzalez, David Ricardo","last_name":"Zapata Gonzalez"},{"last_name":"Brennig","full_name":"Brennig, Katharina","id":"51905","first_name":"Katharina"},{"first_name":"Kay","full_name":"Benkert, Kay","last_name":"Benkert"},{"first_name":"Florian","last_name":"Schlosser","id":"88614","full_name":"Schlosser, Florian"},{"full_name":"Müller, Oliver","id":"72849","last_name":"Müller","first_name":"Oliver"}],"date_created":"2025-12-22T13:13:37Z","title":"Process Mining for Robust Heat Integration through Process- and Product-Centric Energy Demand Modeling","doi":"10.1145/3777518.3777520","main_file_link":[{"url":"https://dl.acm.org/doi/abs/10.1145/3777518.3777520","open_access":"1"}],"publication_identifier":{"issn":["2770-5331","2770-5331"]},"publication_status":"published","issue":"3","year":"2025","page":"19-31","intvolume":"         5","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: <i>ACM SIGEnergy Energy Informatics Review</i>. Vol 5. Association for Computing Machinery (ACM); 2025:19-31. doi:<a href=\"https://doi.org/10.1145/3777518.3777520\">10.1145/3777518.3777520</a>","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 <i>ACM SIGEnergy Energy Informatics Review</i>, 2025, vol. 5, no. 3, pp. 19–31, doi: <a href=\"https://doi.org/10.1145/3777518.3777520\">10.1145/3777518.3777520</a>.","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 <i>ACM SIGEnergy Energy Informatics Review</i>, 5:19–31. Association for Computing Machinery (ACM), 2025. <a href=\"https://doi.org/10.1145/3777518.3777520\">https://doi.org/10.1145/3777518.3777520</a>.","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.","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={<a href=\"https://doi.org/10.1145/3777518.3777520\">10.1145/3777518.3777520</a>}, 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} }","mla":"Zapata Gonzalez, David Ricardo, et al. “Process Mining for Robust Heat Integration through Process- and Product-Centric Energy Demand Modeling.” <i>ACM SIGEnergy Energy Informatics Review</i>, vol. 5, no. 3, Association for Computing Machinery (ACM), 2025, pp. 19–31, doi:<a href=\"https://doi.org/10.1145/3777518.3777520\">10.1145/3777518.3777520</a>.","apa":"Zapata Gonzalez, D. R., Brennig, K., Benkert, K., Schlosser, F., &#38; Müller, O. (2025). Process Mining for Robust Heat Integration through Process- and Product-Centric Energy Demand Modeling. <i>ACM SIGEnergy Energy Informatics Review</i>, <i>5</i>(3), 19–31. <a href=\"https://doi.org/10.1145/3777518.3777520\">https://doi.org/10.1145/3777518.3777520</a>"},"_id":"63397","user_id":"105506","language":[{"iso":"eng"}],"publication":"ACM SIGEnergy Energy Informatics Review","type":"conference","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."}],"status":"public"},{"publication":"ACM SIGEnergy Energy Informatics Review","type":"conference","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."}],"status":"public","_id":"63400","user_id":"105506","language":[{"iso":"eng"}],"publication_identifier":{"issn":["2770-5331","2770-5331"]},"publication_status":"published","issue":"2","year":"2025","intvolume":"         5","page":"4-10","citation":{"mla":"Zapata Gonzalez, David Ricardo, et al. “Causal Machine Learning Approaches for Modelling Data Center Heat Recovery: A Physical Testbed Study.” <i>ACM SIGEnergy Energy Informatics Review</i>, vol. 5, no. 2, Association for Computing Machinery (ACM), 2025, pp. 4–10, doi:<a href=\"https://doi.org/10.1145/3757892.3757893\">10.1145/3757892.3757893</a>.","short":"D.R. Zapata Gonzalez, M. Meyer, O. Müller, in: ACM SIGEnergy Energy Informatics Review, Association for Computing Machinery (ACM), 2025, pp. 4–10.","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={<a href=\"https://doi.org/10.1145/3757892.3757893\">10.1145/3757892.3757893</a>}, 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} }","apa":"Zapata Gonzalez, D. R., Meyer, M., &#38; Müller, O. (2025). Causal Machine Learning Approaches for Modelling Data Center Heat Recovery: A Physical Testbed Study. <i>ACM SIGEnergy Energy Informatics Review</i>, <i>5</i>(2), 4–10. <a href=\"https://doi.org/10.1145/3757892.3757893\">https://doi.org/10.1145/3757892.3757893</a>","ama":"Zapata Gonzalez DR, Meyer M, Müller O. Causal Machine Learning Approaches for Modelling Data Center Heat Recovery: A Physical Testbed Study. In: <i>ACM SIGEnergy Energy Informatics Review</i>. Vol 5. Association for Computing Machinery (ACM); 2025:4-10. doi:<a href=\"https://doi.org/10.1145/3757892.3757893\">10.1145/3757892.3757893</a>","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 <i>ACM SIGEnergy Energy Informatics Review</i>, 2025, vol. 5, no. 2, pp. 4–10, doi: <a href=\"https://doi.org/10.1145/3757892.3757893\">10.1145/3757892.3757893</a>.","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 <i>ACM SIGEnergy Energy Informatics Review</i>, 5:4–10. Association for Computing Machinery (ACM), 2025. <a href=\"https://doi.org/10.1145/3757892.3757893\">https://doi.org/10.1145/3757892.3757893</a>."},"publisher":"Association for Computing Machinery (ACM)","oa":"1","date_updated":"2025-12-22T13:20:02Z","volume":5,"author":[{"id":"105506","full_name":"Zapata Gonzalez, David Ricardo","last_name":"Zapata Gonzalez","first_name":"David Ricardo"},{"last_name":"Meyer","id":"105120","full_name":"Meyer, Marcel","first_name":"Marcel"},{"first_name":"Oliver","last_name":"Müller","full_name":"Müller, Oliver","id":"72849"}],"date_created":"2025-12-22T13:19:06Z","title":"Causal Machine Learning Approaches for Modelling Data Center Heat Recovery: A Physical Testbed Study","doi":"10.1145/3757892.3757893","main_file_link":[{"open_access":"1","url":"https://dl.acm.org/doi/abs/10.1145/3757892.3757893"}]},{"issue":"2","year":"2025","date_created":"2025-12-22T13:17:36Z","publisher":"Association for Computing Machinery (ACM)","title":"Causal Machine Learning Approaches for Modelling Data Center Heat Recovery: A Physical Testbed Study","publication":"ACM SIGEnergy Energy Informatics Review","abstract":[{"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.","lang":"eng"}],"language":[{"iso":"eng"}],"publication_identifier":{"issn":["2770-5331","2770-5331"]},"publication_status":"published","page":"4-10","intvolume":"         5","citation":{"ama":"Gonzalez DZ, Meyer M, Müller O. Causal Machine Learning Approaches for Modelling Data Center Heat Recovery: A Physical Testbed Study. In: <i>ACM SIGEnergy Energy Informatics Review</i>. Vol 5. Association for Computing Machinery (ACM); 2025:4-10. doi:<a href=\"https://doi.org/10.1145/3757892.3757893\">10.1145/3757892.3757893</a>","chicago":"Gonzalez, David Zapata, Marcel Meyer, and Oliver Müller. “Causal Machine Learning Approaches for Modelling Data Center Heat Recovery: A Physical Testbed Study.” In <i>ACM SIGEnergy Energy Informatics Review</i>, 5:4–10. Association for Computing Machinery (ACM), 2025. <a href=\"https://doi.org/10.1145/3757892.3757893\">https://doi.org/10.1145/3757892.3757893</a>.","ieee":"D. Z. Gonzalez, M. Meyer, and O. Müller, “Causal Machine Learning Approaches for Modelling Data Center Heat Recovery: A Physical Testbed Study,” in <i>ACM SIGEnergy Energy Informatics Review</i>, 2025, vol. 5, no. 2, pp. 4–10, doi: <a href=\"https://doi.org/10.1145/3757892.3757893\">10.1145/3757892.3757893</a>.","mla":"Gonzalez, David Zapata, et al. “Causal Machine Learning Approaches for Modelling Data Center Heat Recovery: A Physical Testbed Study.” <i>ACM SIGEnergy Energy Informatics Review</i>, vol. 5, no. 2, Association for Computing Machinery (ACM), 2025, pp. 4–10, doi:<a href=\"https://doi.org/10.1145/3757892.3757893\">10.1145/3757892.3757893</a>.","short":"D.Z. Gonzalez, M. Meyer, O. Müller, in: ACM SIGEnergy Energy Informatics Review, Association for Computing Machinery (ACM), 2025, pp. 4–10.","bibtex":"@inproceedings{Gonzalez_Meyer_Müller_2025, title={Causal Machine Learning Approaches for Modelling Data Center Heat Recovery: A Physical Testbed Study}, volume={5}, DOI={<a href=\"https://doi.org/10.1145/3757892.3757893\">10.1145/3757892.3757893</a>}, 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} }","apa":"Gonzalez, D. Z., Meyer, M., &#38; Müller, O. (2025). Causal Machine Learning Approaches for Modelling Data Center Heat Recovery: A Physical Testbed Study. <i>ACM SIGEnergy Energy Informatics Review</i>, <i>5</i>(2), 4–10. <a href=\"https://doi.org/10.1145/3757892.3757893\">https://doi.org/10.1145/3757892.3757893</a>"},"volume":5,"author":[{"last_name":"Gonzalez","full_name":"Gonzalez, David Zapata","first_name":"David Zapata"},{"full_name":"Meyer, Marcel","last_name":"Meyer","first_name":"Marcel"},{"last_name":"Müller","full_name":"Müller, Oliver","first_name":"Oliver"}],"date_updated":"2025-12-22T13:18:19Z","oa":"1","doi":"10.1145/3757892.3757893","main_file_link":[{"url":"https://dl.acm.org/doi/abs/10.1145/3757892.3757893","open_access":"1"}],"type":"conference","status":"public","user_id":"105506","_id":"63399"}]