References
- B. Donlon, “Quick guide to the integrated single electricity market,” Tech. Rep., 2016. [Online]. Available: https://www.eirgridgroup.com/uuid/1458bec2-f1e3-493c-92de-8dd2228bca1c/EirGrid-Group-I-SEM-Quick-Guide.pdf
- SEM-O, “Market operator performance.” [Online]. Available: http://www.sem-o.com/publications/operator-performance/
- K. Kavanagh, M. Barrett, and M. Conlon, “Short-term electricity load forecasting for the integrated single electricity market (I-SEM),” in 2017 52nd International Universities Power Engineering Conference (UPEC), Heraklion, Greece, Aug. 2017, pp. 1–7. https://doi.org/10.1109/UPEC.2017.8231994
- S. Beltrán, A. Castro, I. Irizar, G. Naveran, and I. Yeregui, “Framework for collaborative intelligence in forecasting day-ahead electricity price,” Applied Energy, vol. 306, Part A, Jan. 2022, Art. no. 118049. https://doi.org/10.1016/j.apenergy.2021.118049
- C. McHugh, S. Coleman, and D. Kerr, “Hourly electricity price forecasting with NARMAX,” Machine Learning with Applications, vol. 9, Sep. 2022, Art. no. 100383. https://doi.org/10.1016/j.mlwa.2022.100383
- C. Lynch, C. O’Leary, P. G. K. Sundareshan, and Y. Akin, “Experimental analysis of GBM to expand the time horizon of Irish electricity price forecasts,” Energies, vol. 14, no. 22, Nov. 2021, Art. no. 7587. https://doi.org/10.3390/en14227587
- C. O’Leary, C. Lynch, R. Bain, G. Smith, and D. Grimes, “A comparison of deep learning vs traditional machine learning for electricity price forecasting,” in 2021 4th International Conference on Information and Computer Technologies (ICICT), HI, USA, Mar. 2021, pp. 6–12. https://doi.org/10.1109/ICICT52872.2021.00009
- D. Grimes, G. Ifrim, B. O’Sullivan, and H. Simonis, “Analyzing the impact of electricity price forecasting on energy cost-aware scheduling,” Sustainable Computing: Informatics and Systems, vol. 4, no. 4, pp. 276– 291, Dec. 2014. https://doi.org/10.1016/j.suscom.2014.08.009
- J. Lago, F. De Ridder, and B. De Schutter, “Forecasting spot electricity prices: Deep learning approaches and empirical comparison of traditional algorithms,” Applied Energy, vol. 221, pp. 386–405, Jul. 2018. https://doi.org/10.1016/j.apenergy.2018.02.069
- A. R. Gollou and N. Ghadimi, “A new feature selection and hybrid forecast engine for day-ahead price forecasting of electricity markets,” Journal of Intelligent and Fuzzy Systems, vol. 32, no. 6, pp. 4031–4045, May 2017. https://doi.org/10.3233/JIFS-152073
- L. Wang, Z. Zhang, and J. Chen, “Short-term electricity price forecasting with stacked denoising autoencoders,” IEEE Transactions on Power Systems, vol. 32, no. 4, pp. 2673–2681, Jul. 2017. https://doi.org/10.1109/TPWRS.2016.2628873
- S. K. Aggarwal, L. M. Saini, and A. Kumar, “Electricity price forecasting in deregulated markets: A review and evaluation,” International Journal of Electrical Power & Energy Systems, vol. 31, no. 1, pp. 13–22, Jan. 2009. https://doi.org/10.1016/j.ijepes.2008.09.003
- U. Ugurlu, I. Oksuz, and O. Tas, “Electricity price forecasting using recurrent neural networks,” Energies, vol. 11, no. 5, May 2018, Art. no. 1255. https://doi.org/10.3390/en11051255
- F. Arci, J. Reilly, P. Li, K. Curran, and A. Belatreche, “Forecasting short-term wholesale prices on the Irish single electricity market,” Int. Journal of Electrical and Computer Engineering (IJECE), vol. 8, no. 6, pp. 4060–4078, Dec. 2018. https://doi.org/10.11591/ijece.v8i6.pp4060-4078
- F. Feijoo, W. Silva, and T. K. Das, “A computationally efficient electricity price forecasting model for real time energy markets,” Energy Conversion and Management, vol. 113, pp. 27–35, Apr. 2016. https://doi.org/10.1016/j.enconman.2016.01.043
- G. Osório, J. Matias, and J. Catalão, “Electricity prices forecasting by a hybrid evolutionary-adaptive methodology,” Energy Conversion and Management, vol. 80, pp. 363–373, Apr. 2014. https://doi.org/10.1016/j.enconman.2014.01.063
- P.-H. Kuo and C.-J. Huang, “An electricity price forecasting model by hybrid structured deep neural networks,” Sustainability, vol. 10, no. 4, Apr. 2018, Art. no. 1280. https://doi.org/10.3390/su10041280
- C. O’Leary, “Capsule networks for electricity price forecasting,” Master’s thesis, Munster Technological University, 2020.
- A. Thessen, “Adoption of machine learning techniques in ecology and Earth science,” One Ecosystem, vol. 1, Jun. 2016, Art. no. e8621. https://doi.org/10.3897/oneeco.1.e8621
- C. O’Leary, F. G. Toosi, and C. Lynch, “A review of AutoML software tools for time series forecasting and anomaly detection,” in Proceedings of the 15th International Conference on Agents and Artificial Intelligence (ICAART), Lisbon, Portugal, Oct. 2023, pp. 421–433. https://doi.org/10.5220/0011683000003393
- X. He, K. Zhao, and X. Chu, “AutoML: A survey of the state-of-the-art,” Knowledge-Based Systems, vol. 212, Jan. 2021, Art. no. 106622. https://doi.org/10.1016/j.knosys.2020.106622
- I. Y. Javeri, M. Toutiaee, I. B. Arpinar, J. A. Miller, and T. W. Miller, “Improving neural networks for time-series forecasting using data augmentation and AutoML,” in 2021 IEEE Seventh International Conference on Big Data Computing Service and Applications (BigDataService), Oxford, United Kingdom, Aug. 2021, pp. 1–8. https://doi.org/10.1109/BigDataService52369.2021.00006
- “Stack overflow developer survey 2023.” [Online]. Available: https://survey.stackoverflow.co/2023/?utmsource=social-share&utmmedium=social&utmcampaign=dev-survey-2023
- C. Lynch, J. Kehoe, R. Bain, F. Zhang, J. Flynn, C. O’Leary, G. Smith, R. Linger, K. Fitzgibbon, and F. Feijoo, “Application of a SVM-based model for day-ahead electricity price prediction for the single electricity market in Ireland,” in 39th International Symposium on Forecasting (ISF), 2019.
- P. Li, F. Arci, J. Reilly, K. Curran, A. Belatreche, and Y. Shynkevich, “Predicting short-term wholesale prices on the Irish single electricity market with artificial neural networks,” in 2017 28th Irish Signals and Systems Conference (ISSC), Killarney, Ireland, Jun. 2017, pp. 1–8. https://doi.org/10.1109/ISSC.2017.7983623
- N. Erickson, J. Mueller, A. Shirkov, H. Zhang, P. Larroy, M. Li, and A. Smola, “AutoGluon-Tabular: robust and accurate AutoML for structured data,” arXiv:2003.06505, Mar. 2020. https://doi.org/10.48550/arXiv.2003.06505
- H. Jin, Q. Song, and X. Hu, “Auto-Keras: An efficient neural architecture search system,” in Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining, ser. KDD’19, New York, NY, USA, Jul. 2019, pp. 1946–1956. https://doi.org/10.1145/3292500.3330648
- C. Catlin, “AutoTS,” Aug. 2022, original-date: 201911-26T14:13:16Z. [Online]. Available: https://github.com/winedarksea/AutoTS
- “tinkoff-ai/etna,” Aug. 2022, original-date: 2021-0827T14:02:56Z. [Online]. Available: https://github.com/tinkoff-ai/etna
- N. O. Nikitin, P. Vychuzhanin, M. Sarafanov, I. S. Polonskaia, I. Revin, I. V. Barabanova, G. Maximov, A. V. Kalyuzhnaya, and A. Boukhanovsky, “Automated evolutionary approach for the design of composite machine learning pipelines,” Future Generation Computer Systems, vol. 127, pp. 109–125, Feb. 2022. https://doi.org/10.1016/j.future.2021.08.022
- C. Wang, Q. Wu, M. Weimer, and E. E. Zhu, “FLAML: A fast and lightweight AutoML library,” Apr. 2021. [Online]. Available: https://proceedings.mlsys.org/paper_files/paper/2021/file/1ccc3bfa05cb37b917068778f3c4523a-Paper.pdf
- M. Ali, “PyCaret: An open source, low-code machine learning library in Python,” 2020. [Online]. Available: https://pypi.org/project/pycaret/2.2.3/
- P. Molino, Y. Dudin, and S. S. Miryala, “Ludwig: a type-based declarative deep learning toolbox,” arXiv:1909.07930, Sep. 2019. https://doi.org/10.48550/arXiv.1909.07930
- M. Feurer, K. Eggensperger, S. Falkner, M. Lindauer, and F. Hutter, “Auto-Sklearn 2.0: Hands-free AutoML via MetaLearning,” arXiv:2007.04074, Sep. 2021. https://doi.org/10.48550/arXiv.2007.04074
- A. Vakhrushev, A. Ryzhkov, M. Savchenko, D. Simakov, R. Damdinov, and A. Tuzhilin, “LightAutoML: AutoML solution for a large financial services ecosystem,” arXiv:2109.01528, Apr. 2022. https://doi.org/10.48550/arXiv.2109.01528
- “AxeldeRomblay / MLBox.” [Online]. Available: https://github.com/AxeldeRomblay/MLBox
- “H2O.ai,” 2022. [Online]. Available: https://github.com/h2oai/h2o-3
- “alteryx/evalml,” Aug. 2022, original-date: 2019-0717T21:36:30Z. [Online]. Available: https://github.com/alteryx/evalml
- S. Makridakis, C. Fry, F. Petropoulos, and E. Spiliotis, “The future of forecasting competitions: Design attributes and principles,” arXiv:2102.04879, May 2021. https://doi.org/10.48550/arXiv.2102.04879
- R. Godahewa, C. Bergmeir, G. I. Webb, R. J. Hyndman, and P. Montero-Manso, “Monash time series forecasting archive,” in 35th Conference on Neural Information Processing Systems, 2021. [Online]. Available: https://openreview.net/pdf?id=I01l7rc0jcb
- A. Bauer, M. Zufle, S. Eismann, J. Grohmann, N. Herbst, and S. Kounev, “Libra: A benchmark for time series forecasting methods,” in Proceedings of the ACM/SPEC International Conference on Performance Engineering, ser. ICPE’21, New York, NY, USA, Apr. 2021, pp. 189–200. https://doi.org/10.1145/3427921.3450241