A No-Code Automated Machine Learning Platform for the Energy Sector

Abstract

This paper presents a No-Code Automated Machine Learning (Auto-ML) platform designed specifically for the energy sector, addressing the challenges of integrating ML in diverse and complex data environments. The proposed platform automates key ML pipeline steps, including data preprocessing, feature engineering, model selection, and hyperparameter tuning, while incorporating domain-specific knowledge to handle unique industry requirements such as fluctuating energy demands and regulatory compliance. The modular architecture allows for customization and scalability, making the platform adaptable across various energy sub-sectors like renewable energy, oil and gas, and power distribution. Our findings highlight the platform's potential to democratize advanced analytical capabilities within the energy industry, enabling non-expert users to generate sophisticated data-driven insights. Preliminary results demonstrate significant improvements in data processing efficiency and predictive accuracy. The paper details the platform's architecture, including data lake and entity-relationship diagrams, and describes the design of user interfaces for data ingestion, preprocessing, model training, and deployment. This study contributes to the field by offering a practical solution to the complexities of ML in the energy sector, facilitating a shift towards more adaptive, efficient, and data-informed operations.

Keywords

• Auto-ML
• No-Code Platform
• Artificial Intelligence
• Energy

References

1. Banzhaf, W. (2006). Introduction. Genetic Programming and Evolvable Machines, 7(1), 5–6. https://doi.org/10.1007/s10710-006-7015-0
2. Browne, C. B., Powley, E., Whitehouse, D., Lucas, S. M., Cowling, P. I., Rohlfshagen, P., Tavener, S., Perez, D., Samothrakis, S., & Colton, S. (2012). A Survey of Monte Carlo Tree Search Methods. IEEE Transactions on Computational Intelligence and AI in Games, 4(1), 1–43. https://doi.org/10.1109/tciaig.2012.2186810
3. Chu, X., Ilyas, I. F., Krishnan, S., & Wang, J. (2016). Data Cleaning. Proceedings of the 2016 International Conference on Management of Data. https://doi.org/10.1145/2882903.2912574
4. Chu, X., Morcos, J., Ilyas, I. F., Ouzzani, M., Papotti, P., Tang, N., & Ye, Y. (2015). KATARA. Proceedings of the 2015 ACM SIGMOD International Conference on Management of Data. https://doi.org/10.1145/2723372.2749431
5. Cubuk, E. D., Zoph, B., Mane, D., Vasudevan, V., & Le, Q. V. (2019). AutoAugment: Learning Augmentation Strategies From Data. 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR). https://doi.org/10.1109/cvpr.2019.00020
6. Darren, C. (2016). Practical ML with H2O: powerful, scalable techniques for deep learning and AI. O’Reilly Media, Inc.
7. Drori, I., Krishnamurthy, Y., Rampin, R., Lourenco, R. d. P., Ono, J. P., Cho, K., Silva, C., & Freire, J. (2018). AlphaD3M: Machine Learning Pipeline Synthesis. In International Conference on Machine Learning AutoML Workshop.
8. Drori, I., Krishnamurthy, Y., de Paula Lourenco, R., Rampin, R., Kyunghyun, C., Silva, C., & Freire, J. (2019). Automatic Machine Learning by Pipeline Synthesis using Model-Based Reinforcement Learning and a Grammar. In International Conference on Machine Learning AutoML Workshop.

9. Erickson, N., Mueller, J., Shirkov, A., Zhang, H., Larroy, P., Li, M., & Smola, A. (2020). AutoGluon-Tabular: Robust and Accurate Auto-ML for Structured Data. arXiv preprint arXiv:2003.06505.
10. Feurer, M., Klein, A., Eggensperger, K., Springenberg, J. T., Blum, M., & Hutter, F. (2019). Auto-sklearn: Efficient and Robust Automated Machine Learning. The Springer Series on Challenges in Machine Learning, 113-134. https://doi.org/10.1007/978-3-030-05318-5_6
11. Gama, J. (2004). Functional Trees. Machine Learning, 55(3), 219–250. https://doi.org/10.1023/b:mach.0000027782.67192.13
12. Ge, P. (2020). Analysis on Approaches and Structures of Automated Machine Learning Frameworks. 2020 International Conference on Communications, Information System and Computer Engineering (CISCE). https://doi.org/10.1109/cisce50729.2020.00106
13. Iwendi, C., Huescas, C. G. Y., Chakraborty, C., & Mohan, S. (2022). COVID-19 health analysis and prediction using machine learning algorithms for Mexico and Brazil patients. Journal of Experimental & Theoretical Artificial Intelligence, 1–21. https://doi.org/10.1080/0952813x.2022.2058097
14. Z. H, J. M., Hossen, J., Sayeed, S., Ho, C., K, T., Rahman, A., & Arif, E. M. H. (2018). A Survey on Cleaning Dirty Data Using Machine Learning Paradigm for Big Data Analytics. Indonesian Journal of Electrical Engineering and Computer Science, 10(3), 1234. https://doi.org/10.11591/ijeecs.v10.i3.pp1234-1243
15. Ji, Z., He, Z., Gui, Y., Li, J., Tan, Y., Wu, B., Xu, R., & Wang, J. (2022). Research and Application Validation of a Feature Wavelength Selection Method Based on Acousto-Optic Tunable Filter (AOTF) and Automatic Machine Learning (AutoML). Materials, 15(8), 2826. https://doi.org/10.3390/ma15082826
16. Jin, H., Song, Q., & Hu, X. (2019). Auto-Keras: An Efficient Neural Architecture Search System. Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining. https://doi.org/10.1145/3292500.3330648
17. Jin, H., Chollet, F., Song, Q., & Hu, X. (2023). Autokeras: an Auto-ML library for deep learning. Journal of Machine Learning Research, 24(6), 1-6. https://www.jmlr.org/papers/volume24/20-1355/20-1355.pdf
18. Kocsis, L., & Szepesvári, C. (2006). Bandit Based Monte-Carlo Planning. Machine Learning: ECML 2006, 282–293. https://doi.org/10.1007/11871842_29
19. Kotthoff, L., Thornton, C., Hoos, H. H., Hutter, F., & Leyton-Brown, K. (2019). Auto-WEKA: Automatic Model Selection and Hyperparameter Optimization in WEKA. The Springer Series on Challenges in Machine Learning, 81–95. https://doi.org/10.1007/978-3-030-05318-5_4
20. Kotthoff, L., Thornton, C., & Hutter, F. (2017). User guide for auto-WEKA version 2.6. Department of Computer Science, University of British Columbia, BETA Lab, Tech Report 2, 1-15. Vancouver, BC, Canada.
21. Koza, JohnR. (1994). Genetic programming as a means for programming computers by natural selection. Statistics and Computing, 4(2). https://doi.org/10.1007/bf00175355
22. Krishnan, S., & Wu, E. (2019). AlphaClean: Automatic generation of data cleaning pipelines. https://doi.org/10.48550/arXiv.1904.11827
23. Lake, B. M., Ullman, T. D., Tenenbaum, J. B., & Gershman, S. J. (2016). Building machines that learn and think like people. Behavioral and Brain Sciences, 40. https://doi.org/10.1017/s0140525x16001837
24. LeDell, E., & Poirier, S. (2020). H2o Auto-ML: scalable automatic machine learning. In 7th ICML Workshop on Automated Machine Learning. https://www.automl.org/wp-content/uploads/2020/07/AutoML_2020_paper_61.pdf
25. LingChen, T. C., Khonsari, A., Lashkari, A., Nazari, M. R., & Sambee, J. S. (2020). UniformAugment: A search-free probabilistic data augmentation approach. arXiv preprint arXiv:2003.14348. https://doi.org/10.48550/arXiv.2003.14348
26. McGushion, H. (2019). HyperparameterHunter. Available at https://github.com/HunterMcGushion/hyperparameter_hunter.
27. Mahdavi, M., Neutatz, F., Visengeriyeva, L., & Abedjan, Z. (2019). Towards automated data cleaning workflows. Machine Learning, 15, 16.
28. Mohr, F., Wever, M., & Hüllermeier, E. (2018). ML-Plan: Automated machine learning via hierarchical planning. Machine Learning, 107(8–10), 1495–1515. https://doi.org/10.1007/s10994-018-5735-z
29. Olson, R. S., Bartley, N., Urbanowicz, R. J., & Moore, J. H. (2016). Evaluation of a Tree-based Pipeline Optimization Tool for Automating Data Science. Proceedings of the Genetic and Evolutionary Computation Conference 2016. https://doi.org/10.1145/2908812.2908918
30. Park, J. B., Lee, K. H., Kwak, J. Y., & Cho, C. S. (2022). Deployment Framework Design Techniques for Optimized Neural Network Applications. 2022 13th International Conference on Information and Communication Technology Convergence (ICTC). https://doi.org/10.1109/ictc55196.2022.9952771
31. Pedregosa, F., Varoquaux, G., & Gramfort, A. (2011). Scikit-learn: ML in python. Journal of Machine Learning Research, 12, 2825-2830.
32. Rakotoarison, H., Schoenauer, M., & Sebag, M. (2019). Automated Machine Learning with Monte-Carlo Tree Search. Proceedings of the Twenty-Eighth International Joint Conference on Artificial Intelligence. https://doi.org/10.24963/ijcai.2019/457
33. Silver, D., Hubert, T., Schrittwieser, J., Antonoglou, I., Lai, M., Guez, A., Lanctot, M., Sifre, L., Kumaran, D., Graepel, T., Lillicrap, T., Simonyan, K., & Hassabis, D. (2018). A general reinforcement learning algorithm that masters chess, shogi, and Go through self-play. Science, 362(6419), 1140–1144. https://doi.org/10.1126/science.aar6404
34. Thornton, C., Hutter, F., Hoos, H. H., & Leyton-Brown, K. (2013). Auto-WEKA. Proceedings of the 19th ACM SIGKDD International Conference on Knowledge Discovery and Data Mining. https://doi.org/10.1145/2487575.2487629
35. UC Irvine ML Repository. (2023). Epileptic Seizures Dataset. https://www.kaggle.com/datasets/chaditya95/epileptic-seizures-dataset
36. Vafaie, H., & Jong, K. (1998). Evolutionary Feature Space Transformation. Feature Extraction, Construction and Selection, 307–323. https://doi.org/10.1007/978-1-4615-5725-8_19
37. Zheng, Z. (1998). A Comparison of Constructing Different Types of New Feature For Decision Tree Learning. Feature Extraction, Construction and Selection, 239-255. https://doi.org/10.1007/978-1-4615-5725-8_15