{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,5]],"date-time":"2026-06-05T05:53:23Z","timestamp":1780638803431,"version":"3.54.1"},"reference-count":41,"publisher":"Walter de Gruyter GmbH","issue":"2","license":[{"start":{"date-parts":[[2024,12,1]],"date-time":"2024-12-01T00:00:00Z","timestamp":1733011200000},"content-version":"unspecified","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":[],"published-print":{"date-parts":[[2024,12,1]]},"abstract":"<jats:title>Abstract<\/jats:title>\n                  <jats:p>Reliable and accurate electricity price forecasting algorithms can be used to inform efficient energy consumption schedules and maximise profits for electricity traders. Operating within Ireland\u2019s Integrated Single Electricity Market (I-SEM), traders can buy and sell electricity at fluctuating hourly rates whose day-ahead prices are published at approximately 13:00 GMT day-1. Access to electricity price predictions earlier than this publication time allows stakeholders an expanded timeframe to facilitate energy cost-aware scheduling.<\/jats:p>\n                  <jats:p>While many studies have been conducted to espouse various machine learning and statistical approaches to electricity price forecasting, these models tend to be bespoke and require in-depth knowledge regarding model implementation. The problem of requiring such expertise is not unique to time series forecasting, and research into mitigating such limitations exists in the form of Automated Machine Learning (AutoML). AutoML aims to derive effective models while automating various steps typically required for machine learning experimentation, such as pre-processing, model selection, validation, etc.<\/jats:p>\n                  <jats:p>Given the increasing proliferation of AutoML tools and frameworks, this paper applies eight Python-based AutoML libraries to day-ahead electricity price forecasting on an excerpt of I-SEM data. These libraries are compared across a series of error metrics and training times to produce an empirical benchmark that can be utilised to select high-performing AutoML tools for further price forecasting research and other forms of time series forecasting. AutoKeras is found to produce accurate forecasts but requires careful configuration to avoid long runtimes. PyCaret, Ludwig, FLAML and FEDOT also generate favourable results while being significantly easier to configure.<\/jats:p>","DOI":"10.2478\/acss-2024-0020","type":"journal-article","created":{"date-parts":[[2024,12,6]],"date-time":"2024-12-06T13:12:43Z","timestamp":1733490763000},"page":"43-52","source":"Crossref","is-referenced-by-count":4,"title":["A Comparative Analysis of Automated Machine Learning Libraries for Electricity Price Forecasting"],"prefix":"10.2478","volume":"29","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9398-6950","authenticated-orcid":false,"given":"Christian","family":"O\u2019Leary","sequence":"first","affiliation":[{"name":"Department of Computer Science , Munster Technological University , Cork , Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Conor","family":"Lynch","sequence":"additional","affiliation":[{"name":"Nimbus Research Centre , Munster Technological University , Cork , Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1105-4819","authenticated-orcid":false,"given":"Farshad Ghassemi","family":"Toosi","sequence":"additional","affiliation":[{"name":"Department of Computer Science , Munster Technological University , Cork , Ireland"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"374","published-online":{"date-parts":[[2024,12,6]]},"reference":[{"key":"2026060417122774506_j_acss-2024-0020_ref_001","unstructured":"B. Donlon, \u201cQuick guide to the integrated single electricity market,\u201d Tech. Rep., 2016. [Online]. Available: https:\/\/www.eirgridgroup.com\/uuid\/1458bec2-f1e3-493c-92de-8dd2228bca1c\/EirGrid-Group-I-SEM-Quick-Guide.pdf"},{"key":"2026060417122774506_j_acss-2024-0020_ref_002","unstructured":"SEM-O, \u201cMarket operator performance.\u201d [Online]. Available: http:\/\/www.sem-o.com\/publications\/operator-performance\/"},{"key":"2026060417122774506_j_acss-2024-0020_ref_003","doi-asserted-by":"crossref","unstructured":"K. Kavanagh, M. Barrett, and M. Conlon, \u201cShort-term electricity load forecasting for the integrated single electricity market (I-SEM),\u201d in 2017 52nd International Universities Power Engineering Conference (UPEC), Heraklion, Greece, Aug. 2017, pp. 1\u20137. https:\/\/doi.org\/10.1109\/UPEC.2017.8231994","DOI":"10.1109\/UPEC.2017.8231994"},{"key":"2026060417122774506_j_acss-2024-0020_ref_004","doi-asserted-by":"crossref","unstructured":"S. Beltr\u00e1n, A. Castro, I. Irizar, G. Naveran, and I. Yeregui, \u201cFramework for collaborative intelligence in forecasting day-ahead electricity price,\u201d Applied Energy, vol. 306, Part A, Jan. 2022, Art. no. 118049. https:\/\/doi.org\/10.1016\/j.apenergy.2021.118049","DOI":"10.1016\/j.apenergy.2021.118049"},{"key":"2026060417122774506_j_acss-2024-0020_ref_005","doi-asserted-by":"crossref","unstructured":"C. McHugh, S. Coleman, and D. Kerr, \u201cHourly electricity price forecasting with NARMAX,\u201d Machine Learning with Applications, vol. 9, Sep. 2022, Art. no. 100383. https:\/\/doi.org\/10.1016\/j.mlwa.2022.100383","DOI":"10.1016\/j.mlwa.2022.100383"},{"key":"2026060417122774506_j_acss-2024-0020_ref_006","doi-asserted-by":"crossref","unstructured":"C. Lynch, C. O\u2019Leary, P. G. K. Sundareshan, and Y. Akin, \u201cExperimental analysis of GBM to expand the time horizon of Irish electricity price forecasts,\u201d Energies, vol. 14, no. 22, Nov. 2021, Art. no. 7587. https:\/\/doi.org\/10.3390\/en14227587","DOI":"10.3390\/en14227587"},{"key":"2026060417122774506_j_acss-2024-0020_ref_007","doi-asserted-by":"crossref","unstructured":"C. O\u2019Leary, C. Lynch, R. Bain, G. Smith, and D. Grimes, \u201cA comparison of deep learning vs traditional machine learning for electricity price forecasting,\u201d in 2021 4th International Conference on Information and Computer Technologies (ICICT), HI, USA, Mar. 2021, pp. 6\u201312. https:\/\/doi.org\/10.1109\/ICICT52872.2021.00009","DOI":"10.1109\/ICICT52872.2021.00009"},{"key":"2026060417122774506_j_acss-2024-0020_ref_008","doi-asserted-by":"crossref","unstructured":"D. Grimes, G. Ifrim, B. O\u2019Sullivan, and H. Simonis, \u201cAnalyzing the impact of electricity price forecasting on energy cost-aware scheduling,\u201d Sustainable Computing: Informatics and Systems, vol. 4, no. 4, pp. 276\u2013 291, Dec. 2014. https:\/\/doi.org\/10.1016\/j.suscom.2014.08.009","DOI":"10.1016\/j.suscom.2014.08.009"},{"key":"2026060417122774506_j_acss-2024-0020_ref_009","doi-asserted-by":"crossref","unstructured":"J. Lago, F. De Ridder, and B. De Schutter, \u201cForecasting spot electricity prices: Deep learning approaches and empirical comparison of traditional algorithms,\u201d Applied Energy, vol. 221, pp. 386\u2013405, Jul. 2018. https:\/\/doi.org\/10.1016\/j.apenergy.2018.02.069","DOI":"10.1016\/j.apenergy.2018.02.069"},{"key":"2026060417122774506_j_acss-2024-0020_ref_010","doi-asserted-by":"crossref","unstructured":"A. R. Gollou and N. Ghadimi, \u201cA new feature selection and hybrid forecast engine for day-ahead price forecasting of electricity markets,\u201d Journal of Intelligent and Fuzzy Systems, vol. 32, no. 6, pp. 4031\u20134045, May 2017. https:\/\/doi.org\/10.3233\/JIFS-152073","DOI":"10.3233\/JIFS-152073"},{"key":"2026060417122774506_j_acss-2024-0020_ref_011","doi-asserted-by":"crossref","unstructured":"L. Wang, Z. Zhang, and J. Chen, \u201cShort-term electricity price forecasting with stacked denoising autoencoders,\u201d IEEE Transactions on Power Systems, vol. 32, no. 4, pp. 2673\u20132681, Jul. 2017. https:\/\/doi.org\/10.1109\/TPWRS.2016.2628873","DOI":"10.1109\/TPWRS.2016.2628873"},{"key":"2026060417122774506_j_acss-2024-0020_ref_012","doi-asserted-by":"crossref","unstructured":"S. K. Aggarwal, L. M. Saini, and A. 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