Ahmad, T., Zhu, H., Zhang, D., Tariq, R., Bassam, A., Ullah, F., Al Ghamdi, A. S., & Alshamrani, S. S. (2022). Energetics systems and artificial intelligence: applications of industry 4.0. Energy Reports, 8, 334-361. http://dx.doi.org/10.1016/j.egyr.2021.11.256.
Alvela Nieto, M. T., Nabati, E. G., Bode, D., Redecker, M.A., Decker, A., & Thoben, K.-D. (2019). Enabling energy efficiency in manufacturing environments through deep learning approaches: lessons learned. Cham: Springer. http://dx.doi.org/10.1007/978-3-030-29996-5_65.
Alvela Nieto, M. T., Nabati, E. G., & Thoben, K.-D. (2021). Energy transparency in compound feed production. In IFIP International Conference on Advances in Production Management Systems. Cham: Springer. http://dx.doi.org/10.1007/978-3-030-85914-5_53.
AutoML. (2022). Automated Machine Learning. Retrieved in 2022, March 29, from https://www.automl.org/
Azure, M. (2022). What is automated machine learning? Retrieved in 2022, March 29, from http://docs. microsoft.com/en-us/azure/machine-learning concept-automated-ml
Blesl, M., & Kessler, A. (2018). Energieeffizienz in der industrie. Berlin: Springer-Verlag GmbH. https://doi.org/10.1007/978-3-662-55999-4.
Botelho, S. S. C., Filho, N. D., Espindola, D., Amaral, M., Emmendorfer, L., Penna, R., Frazzon, E. M., Pereira, C. E., & Ventura, R. (2014). Including operator’s skill and environment conditions in IMS. In 2014 12th IEEE International Conference on Industrial Informatics (INDIN) (pp. 295-300). New York: IEEE. http://dx.doi.org/10.1109/INDIN.2014.6945527.
Bundesamt für Wirtschaft und Ausfuhrkontrolle – BAFA. (2020). Energieaudit. Retrieved in 2020, December 5, from https://www.bafa.de/DE/Energie/Energieeffizienz/Energieaudit/energieaudit_node.html
Daigneau, R. (2012). Service design patterns: fundamental design solutions for SOAP/WSDL and RESTful web services. Amsterdam: Addison-Wesley.
Darwish, M., Shehab, E., Al-Ashaab, A., & Haque, B. (2010). Value stream mapping and analysis of product development (engineering) process. In Proceedings of the 8th International Conference on Manufacturing Research (ICMR), (pp. 14-16). UK: University Durham.
Deutsches Institut für Normung – DIN. (2015). DIN EN ISO 9001:2008. Retrieved in 2022, April 13, from https://www.din.de/de/wdc-beuth:din21:235671251
DevOps. (2020). Where the world meets. Retrieved in 2020, August 10, from https://devops.com/
DuttaGupta, A. (2017). Energy efficiency using machine learning–targeting small and medium-sized manufactures. In Proceedings of the IIE Annual Conference (pp. 976-981). Norcross: Institute of Industrial and Systems Engineers.
Gleich, R., Bartels, P., & Breisig, V. (2012). Nachhaltigkeitscontrolling: konzepte, instrumente und fallbeispiele für die umsetzung. Germany: Haufe-Gruppe.
Goodfellow, I., Bengio, Y., & Courville, A. (2016). Deep learning. Cambridge: MIT Press. Retrieved in 2020, September 15, from http://www.deeplearningbook.org/
Hecker, D., Döbel, I., Rüping, S., & Schmitz, V. (2017). Künstliche Intelligenz und die Potenziale des maschinellen Lernens für die Industrie. Wirtschaftsinformatik & Management, 9(5), 26-35. http://dx.doi.org/10.1007/s35764-017-0110-6.
Herwig, H. (2016). Energie: richtig bewerten und sinnvoll nutzen: essentials (1st ed.). Wiesbaden: Springer-Verlag. http://dx.doi.org/10.1007/978-3-658-12920-0.
IBM. (2011). IBM-SPSS modeler crisp-dm guide. Retrieved in 2020, April 28, from https://www.ibm.com/docs/en/spss-modeler/18.1.1?topic=spss-modeler-crisp-dm-guide
International Energy Agency – IEA. (2017). Manufacturing energy consumption by subsector in selected IEA countries. Retrieved in 2020, September 15, from https://www.iea.org/data-and-statistics/charts/ manufacturing-energy-consumption-by-subsector-in-selected-iea-countries-2017
International Organization for Standardization – ISO. (2008). Software engineering: software product quality requirements and evaluation (SQuaRE): data quality model. Geneva: ISO. Retrieved in 2022, April 15, from https://www.iso.org/standard/35736.html
International Organization for Standardization – ISO. (2018). ISO 50001: energy management. Geneva: ISO. Retrieved in 2021, September 15, from https://www. iso.org/iso-50001-energy-management.html
International Organization for Standardization – ISO. (2019). Data quality: part 63: data quality management: process measurement. Geneva: ISO. Retrieved in 2022, April 15, from https://www.iso.org/standard/65344.html
Irrek, W., & Thomas, S. (2008). Defining energy efficiency. Retrieved in 2022, April 27, from https://wupperinst.org/uploads/tx_wupperinst/energy_efficiency_ definition.pdf
Kleppmann, W. (2016). Versuchsplanung: produkte und prozesse optimieren (Praxisreihe Qualitätswissen). München: Hanser.
Metaflow. (2021). A framework for real-life data science. Retrieved in 2021, July 11, from https: //metaflow.org/
Mills, E., Shamshoian, G., Blazek, M., Naughton, P., Seese, R. S., Tschudi, W., & Sartor, D. (2008). The business case for energy management in high-tech industries. Energy Efficiency, 1(1), 5-20. http://dx.doi.org/10.1007/s12053-007-9000-8.
MLOps. (2022). Machine Learning Operations. Retrieved in 2022, March 29, from https://ml-ops.org/
Monostori, L. (2003). AI and machine learning techniques for managing complexity, changes and uncertainties in manufacturing. . Engineering Applications of Artificial Intelligence, 16, 277-291. http://dx.doi.org/10.1016/S0952-1976(03)00078-2.
Nabati, E. G., Alvela Nieto, M. T., Decker, A., & Thoben, K.-D. (2020). Application of virtual reality technologies for achieving energy efficient manufacturing: literature analysis and findings. In IFIP International Conference on Advances in Production Management Systems. Cham: Springer. http://dx.doi.org/10.1007/978-3-030-57993-7_54.
Narciso, D. A., & Martins, F. (2020). Application of machine learning tools for energy efficiency in industry: a review. Energy Reports, 6, 1181-1199. http://dx.doi.org/10.1016/j.egyr.2020.04.035.
OPCFoundation. (2020). Overview. Retrieved in 2020, October 20, from https://reference.opcfoundation.org/v104/
Pyvovar, N. (2019). Data science project management methodologies. Retrieved in 2022, April 19 , from https://medium.datadriveninvestor.com/data-science-project-management-methodologies-f6913c6b29eb
Roy, J., & Ramanujan, A. (2001). Understanding Web services. IT Professional, 3(6), 69-73. http://dx.doi.org/10.1109/6294.977775.
Schaefer, J. L., Carvalho, P. S., Ruhoff, A., Thomas, J. D., & Siluk, J. C. M. (2021). Permeability evaluation of Industry 4.0 technologies in cloud-based energy management systems environments - Energy Cloud. Production, 31, e20210038. http://dx.doi.org/10.1590/0103-6513.20210038.
Scikit Learn. (2007). Novelty and outlier detection. Retrieved in 2020, October 5, from https://scikit-learn.org/ stable/modules/outlier_detection.html
Scrum Alliance. (2015). Overview: what is Scrum? Retrieved in 2020, July 11, from https://www.scrumalliance.org/about-scrum
Sen, D., Ozturk, M., & Vayvay, O. (2016). An overview of big data for growth in SMEs. Procedia: Social and Behavioral Sciences, 235, 159-167. http://dx.doi.org/10.1016/j.sbspro.2016.11.011.
Seow, Y., & Rahimifard, S. (2011). A framework for modelling energy consumption within manufacturing systems. CIRP Journal of Manufacturing Science and Technology, 4(3), 258-264. http://dx.doi.org/10.1016/j.cirpj.2011.03.007.
Song, B., Ao, Y., Xiang, L., & Lionel, K. (2018). Data-driven approach for discovery of energy saving potentials in manufacturing factory. Procedia CIRP, 69, 330-335. http://dx.doi.org/10.1016/j.procir.2017.11.143.
Swagger. (2020). API development for everyone. Retrieved in 2020, November 18, from https://swagger.io/
Tan, D., Suvarna, M., Tan, Y. S., Li, J., & Wang, X. (2021). A three-step machine learning framework for energy profiling, activity state prediction and production estimation in smart process manufacturing. Applied Energy, 291, 116808. http://dx.doi.org/10.1016/j.apenergy.2021.116808.
Thiede, S. (2012). Energy efficiency in manufacturing systems. In C. Herrmann & S. Kara (Eds.), Sustainable production, life cycle engineering and management. Berlin: Springer.
Thiede, S., Turetskyy, A., Loellhoeffel, T., Kwade, A., Kara, S., & Herrmann, C. (2020). Machine learning approach for systematic analysis of energy efficiency potentials in manufacturing processes: a case of battery production. CIRP Annals, 69(1), 21-24. http://dx.doi.org/10.1016/j.cirp.2020.04.090.
Tuev. (2020). Energy audit according to DIN EN 162471. Retrieved in 2020, December 5, from https://www.tuev-nord.de/en/company/energy/energy-efficiency/energy-efficiency-in-trade-and-industry/energy-audit-according-to-din-en-16247-1/
Uber. (2017). Meet Michelangelo: Uber’s Machine Learning Platform. Retrieved in 2021, December 12, from https://eng.uber.com/michelangelo-machine-learning-platform/
Verein Deutscher Ingenieure – VDI. (2019). VDI 4663 blatt 1: bewertung von energie- und stoffeffizienz - methodische anwendung des physikalischen optimums. Berlin: VDI.
Vilalta, R., & Drissi, Y. (2002). A perspective view and survey of metalearning. Artificial Intelligence Review, 18(2), 77-95. http://dx.doi.org/10.1023/A:1019956318069.
Yu, L., & Liu, H. (2004). Efficient feature selection via analysis of relevance and redundancy. Journal of Machine Learning Research, 5, 1205-1224.
Zhong, Q., Tang, R., Lv, J., Jia, S., & Jin, M. (2016). Evaluation on models of calculating energy consumption in metal cutting processes: a case of external turning process. International Journal of Advanced Manufacturing Technology, 82(9-12), 2087-2099. http://dx.doi.org/10.1007/s00170-015-7477-4.