---
_id: '64870'
author:
- first_name: Marcel
full_name: Meyer, Marcel
id: '105120'
last_name: Meyer
orcid: ' 0009-0005-9136-8525'
- first_name: David Ricardo
full_name: Zapata Gonzalez, David Ricardo
id: '105506'
last_name: Zapata Gonzalez
- 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: Meyer M, Zapata Gonzalez DR, Kaltenpoth SB, Müller O. Benchmarking Time Series
Foundation Models for Short-Term Household Electricity Load Forecasting. IEEE
Access. 2025;13:218141-218153. doi:10.1109/access.2025.3648056
apa: Meyer, M., Zapata Gonzalez, D. R., Kaltenpoth, S. B., & Müller, O. (2025).
Benchmarking Time Series Foundation Models for Short-Term Household Electricity
Load Forecasting. IEEE Access, 13, 218141–218153. https://doi.org/10.1109/access.2025.3648056
bibtex: '@article{Meyer_Zapata Gonzalez_Kaltenpoth_Müller_2025, title={Benchmarking
Time Series Foundation Models for Short-Term Household Electricity Load Forecasting},
volume={13}, DOI={10.1109/access.2025.3648056},
journal={IEEE Access}, publisher={Institute of Electrical and Electronics Engineers
(IEEE)}, author={Meyer, Marcel and Zapata Gonzalez, David Ricardo and Kaltenpoth,
Sascha Benjamin and Müller, Oliver}, year={2025}, pages={218141–218153} }'
chicago: 'Meyer, Marcel, David Ricardo Zapata Gonzalez, Sascha Benjamin Kaltenpoth,
and Oliver Müller. “Benchmarking Time Series Foundation Models for Short-Term
Household Electricity Load Forecasting.” IEEE Access 13 (2025): 218141–53.
https://doi.org/10.1109/access.2025.3648056.'
ieee: 'M. Meyer, D. R. Zapata Gonzalez, S. B. Kaltenpoth, and O. Müller, “Benchmarking
Time Series Foundation Models for Short-Term Household Electricity Load Forecasting,”
IEEE Access, vol. 13, pp. 218141–218153, 2025, doi: 10.1109/access.2025.3648056.'
mla: Meyer, Marcel, et al. “Benchmarking Time Series Foundation Models for Short-Term
Household Electricity Load Forecasting.” IEEE Access, vol. 13, Institute
of Electrical and Electronics Engineers (IEEE), 2025, pp. 218141–53, doi:10.1109/access.2025.3648056.
short: M. Meyer, D.R. Zapata Gonzalez, S.B. Kaltenpoth, O. Müller, IEEE Access 13
(2025) 218141–218153.
date_created: 2026-03-09T16:58:28Z
date_updated: 2026-03-10T08:13:21Z
doi: 10.1109/access.2025.3648056
intvolume: ' 13'
language:
- iso: eng
page: 218141-218153
publication: IEEE Access
publication_identifier:
issn:
- 2169-3536
publication_status: published
publisher: Institute of Electrical and Electronics Engineers (IEEE)
status: public
title: Benchmarking Time Series Foundation Models for Short-Term Household Electricity
Load Forecasting
type: journal_article
user_id: '105120'
volume: 13
year: '2025'
...
---
_id: '60680'
abstract:
- lang: eng
text: "Classical machine learning techniques often struggle with overfitting and
unreliable predictions when exposed to novel conditions. Introducing causality
into the modelling process offers a promising way to mitigate these challenges
by enhancing predictive robustness. However, constructing an initial causal graph
manually using domain knowledge is time-consuming, particularly in complex time
series with numerous variables. To address this, causal discovery algorithms can
provide a preliminary causal structure that domain experts can refine. This study
investigates causal feature selection with domain knowledge using a data center
system as an example. We use simulated time-series data to compare \r\ndifferent
causal feature selection with traditional machine-learning feature selection methods.
Our results show that predictions based on causal features are more robust compared
to those derived from traditional methods. These findings underscore the potential
of combining causal discovery algorithms with human expertise to improve machine
learning applications."
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. Bridging the gap between data-driven
and theory-driven modelling – leveraging causal machine learning for integrative
modelling of dynamical systems. In: ; 2025.'
apa: Zapata Gonzalez, D. R., Meyer, M., & Müller, O. (2025). Bridging the
gap between data-driven and theory-driven modelling – leveraging causal machine
learning for integrative modelling of dynamical systems. European Conference
on Information Systems, Amman, Jordan.
bibtex: '@inproceedings{Zapata Gonzalez_Meyer_Müller_2025, title={Bridging the gap
between data-driven and theory-driven modelling – leveraging causal machine learning
for integrative modelling of dynamical systems}, author={Zapata Gonzalez, David
Ricardo and Meyer, Marcel and Müller, Oliver}, year={2025} }'
chicago: Zapata Gonzalez, David Ricardo, Marcel Meyer, and Oliver Müller. “Bridging
the Gap between Data-Driven and Theory-Driven Modelling – Leveraging Causal Machine
Learning for Integrative Modelling of Dynamical Systems,” 2025.
ieee: D. R. Zapata Gonzalez, M. Meyer, and O. Müller, “Bridging the gap between
data-driven and theory-driven modelling – leveraging causal machine learning for
integrative modelling of dynamical systems,” presented at the European Conference
on Information Systems, Amman, Jordan, 2025.
mla: Zapata Gonzalez, David Ricardo, et al. Bridging the Gap between Data-Driven
and Theory-Driven Modelling – Leveraging Causal Machine Learning for Integrative
Modelling of Dynamical Systems. 2025.
short: 'D.R. Zapata Gonzalez, M. Meyer, O. Müller, in: 2025.'
conference:
end_date: 18.06.2025
location: Amman, Jordan
name: European Conference on Information Systems
start_date: 16.06.2025
date_created: 2025-07-21T07:52:03Z
date_updated: 2025-07-22T06:30:37Z
department:
- _id: '196'
keyword:
- Causal Machine Learning
- Causality in Time Series
- Causal Discovery
- Human-Machine Collaboration
language:
- iso: eng
main_file_link:
- url: https://aisel.aisnet.org/ecis2025/bus_analytics/bus_analytics/2/
status: public
title: Bridging the gap between data-driven and theory-driven modelling – leveraging
causal machine learning for integrative modelling of dynamical systems
type: conference
user_id: '72849'
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'
...