{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,6,24]],"date-time":"2026-06-24T20:47:11Z","timestamp":1782334031579,"version":"3.54.5"},"reference-count":39,"publisher":"Wiley","issue":"9","license":[{"start":{"date-parts":[[2025,7,24]],"date-time":"2025-07-24T00:00:00Z","timestamp":1753315200000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/2.zoppoz.workers.dev:443\/http\/creativecommons.org\/licenses\/by-nc\/4.0\/"}],"content-domain":{"domain":["onlinelibrary.wiley.com"],"crossmark-restriction":true},"short-container-title":["Expert Systems"],"published-print":{"date-parts":[[2025,9]]},"abstract":"<jats:title>ABSTRACT<\/jats:title><jats:p>Construction planning is a critical and complex phase in the deployment of large\u2010scale renewable energy infrastructure. This study applies artificial intelligence techniques to a domain\u2010specific problem that has traditionally relied on expert judgement: the generation of detailed construction schedules for photovoltaic power plants. As renewable generation is a key part to meet the challenges of energy transition, the implementation of large projects has increased in recent years and this trend is expected to continue in the future. The main difficulty in meeting construction deadlines is the elaboration of an adequate planning. A tool that automatically generates schedules can be of great help to set up an initial baseline planning. To this end, this work compares five artificial intelligence techniques, on a data set consisting of real examples of successfully completed projects. The evaluation of the results obtained on test data shows that Adaptive Neuro\u2010Fuzzy Inference System (ANFIS) is the technique that obtains the best performance in all error metrics, although it entails a high computational cost. The model thus obtained manages to generate a complete construction schedule with an error of 8% of the total duration. The use of metrics as MAE, MSE and  provides a robust understanding of prediction accuracy, variability, and fit. These metrics are commonly used in project planning evaluations and help interpret model behaviour under different error profiles. Additionally, the resulting 8% total duration error implies a deviation of around 24\u2009days in a 300\u2010day project, which is highly actionable in real\u2010world solar project management. The findings not only demonstrate the feasibility of using AI for solar construction planning, but also lay the groundwork for the development of intelligent software tools or platforms that could support planners in the renewable energy sector. While this study focuses on photovoltaic plants, the approach is extendable to other power plants as wind farms, combined\u2010cycle or nuclear plants, or even to other construction projects.<\/jats:p>","DOI":"10.1111\/exsy.70105","type":"journal-article","created":{"date-parts":[[2025,7,25]],"date-time":"2025-07-25T03:55:07Z","timestamp":1753415707000},"update-policy":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1002\/crossmark_policy","source":"Crossref","is-referenced-by-count":1,"title":["Artificial Intelligent Application in Project Management: An Algorithm Comparison for Solar Plants Planning Construction"],"prefix":"10.1111","volume":"42","author":[{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0009-0007-2477-7489","authenticated-orcid":false,"given":"Manuel \u00c1ngel","family":"L\u00f3pez Ferreiro","sequence":"first","affiliation":[{"name":"UNIR, International University of La Rioja  Logro\u00f1o La Rioja Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"given":"Jes\u00fas Gil","family":"Ruiz","sequence":"additional","affiliation":[{"name":"Universidad Europea de Madrid  Madrid Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0002-8645-055X","authenticated-orcid":false,"given":"\u00d3scar","family":"Garc\u00eda","sequence":"additional","affiliation":[{"name":"UNIR, International University of La Rioja  Logro\u00f1o La Rioja Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/2.zoppoz.workers.dev:443\/https\/orcid.org\/0000-0001-9727-315X","authenticated-orcid":false,"given":"Luis","family":"De La Fuente Valent\u00edn","sequence":"additional","affiliation":[{"name":"UNIR, International University of La Rioja  Logro\u00f1o La Rioja Spain"}],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"311","published-online":{"date-parts":[[2025,7,24]]},"reference":[{"key":"e_1_2_7_2_1","doi-asserted-by":"publisher","DOI":"10.1061\/(ASCE)0733-9364(1997)123:4(450)"},{"key":"e_1_2_7_3_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10845-015-1146-1"},{"key":"e_1_2_7_4_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.cor.2010.01.007"},{"key":"e_1_2_7_5_1","doi-asserted-by":"publisher","DOI":"10.1108\/ECAM\u201003\u20102016\u20100085"},{"key":"e_1_2_7_6_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00170-015-6795-x"},{"key":"e_1_2_7_7_1","unstructured":"Brownlee J.2016.XGBoost with Python: Gradient Boosted Trees With XGBoost and Scikit\u2010learn."},{"key":"e_1_2_7_8_1","doi-asserted-by":"publisher","DOI":"10.1142\/S0219686723500099"},{"key":"e_1_2_7_9_1","doi-asserted-by":"publisher","DOI":"10.1061\/(ASCE)CO.1943-7862.0001697"},{"issue":"4","key":"e_1_2_7_10_1","first-page":"5156","article-title":"Artificial Intelligence Approaches to Dynamic Project Success Assessment Taxonomic","volume":"9","author":"Cheng M.\u2010Y.","year":"2012","journal-title":"Life Science Journal"},{"key":"e_1_2_7_11_1","first-page":"512","volume-title":"26th International Symposium on Automation and Robotics in Construction (isarc2009)","author":"Cheng M.\u2010Y.","year":"2009"},{"key":"e_1_2_7_12_1","doi-asserted-by":"publisher","DOI":"10.1007\/s00170-015-7339-0"},{"key":"e_1_2_7_13_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-018-9667-6"},{"key":"e_1_2_7_14_1","doi-asserted-by":"publisher","DOI":"10.9781\/ijimai.2020.12.003"},{"key":"e_1_2_7_15_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.dss.2007.03.013"},{"key":"e_1_2_7_16_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.jclepro.2024.143681"},{"key":"e_1_2_7_17_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.eswa.2020.114116"},{"key":"e_1_2_7_18_1","unstructured":"Harvey M.2017.\u201cLet's Evolve A Neural Network With a Genetic Algorithm \u201dCoastline Automation."},{"key":"e_1_2_7_19_1","volume-title":"Renewable Energy Statistics 2023","author":"IRENA","year":"2023"},{"key":"e_1_2_7_20_1","doi-asserted-by":"publisher","DOI":"10.1109\/21.256541"},{"key":"e_1_2_7_21_1","doi-asserted-by":"publisher","DOI":"10.7232\/iems.2012.11.3.215"},{"key":"e_1_2_7_22_1","doi-asserted-by":"publisher","DOI":"10.1016\/S0926-5805(02)00091-2"},{"key":"e_1_2_7_23_1","unstructured":"Meggs T.2023.\u201cANFIS: Python implementation of an Adaptive Neuro Fuzzy Inference System.\u201dGitHub.https:\/\/2.zoppoz.workers.dev:443\/https\/github.com\/twmeggs\/anfis."},{"key":"e_1_2_7_24_1","doi-asserted-by":"publisher","DOI":"10.21105\/joss.00433"},{"key":"e_1_2_7_25_1","volume-title":"Machine Learning","author":"Mitchell T. M.","year":"1997"},{"key":"e_1_2_7_26_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10462\u2010009\u20109137\u20102"},{"key":"e_1_2_7_27_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ejor.2019.01.063"},{"key":"e_1_2_7_28_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-030-50153-2_36"},{"key":"e_1_2_7_29_1","doi-asserted-by":"publisher","DOI":"10.3390\/joitmc8010045"},{"key":"e_1_2_7_30_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-0-387-09492-2_25"},{"key":"e_1_2_7_31_1","volume-title":"Artificial Intelligence: A Modern Approach","author":"Russell S.","year":"2020"},{"key":"e_1_2_7_32_1","doi-asserted-by":"publisher","DOI":"10.1108\/978-1-78743-868-220181002"},{"key":"e_1_2_7_33_1","doi-asserted-by":"publisher","DOI":"10.1162\/106365602320169811"},{"key":"e_1_2_7_34_1","doi-asserted-by":"publisher","DOI":"10.1007\/s10462-022-10188-3"},{"key":"e_1_2_7_35_1","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4842-9751-3_4"},{"key":"e_1_2_7_36_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.autcon.2020.103348"},{"key":"e_1_2_7_37_1","doi-asserted-by":"publisher","DOI":"10.1038\/s41592-019-0686-2"},{"key":"e_1_2_7_38_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ijproman.2005.06.006"},{"key":"e_1_2_7_39_1","doi-asserted-by":"publisher","DOI":"10.1007\/s11047-021-09855-1"},{"key":"e_1_2_7_40_1","doi-asserted-by":"publisher","DOI":"10.1016\/j.ress.2025.110885"}],"container-title":["Expert Systems"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/onlinelibrary.wiley.com\/doi\/pdf\/10.1111\/exsy.70105","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2025,8,20]],"date-time":"2025-08-20T01:33:56Z","timestamp":1755653636000},"score":1,"resource":{"primary":{"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/onlinelibrary.wiley.com\/doi\/10.1111\/exsy.70105"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2025,7,24]]},"references-count":39,"journal-issue":{"issue":"9","published-print":{"date-parts":[[2025,9]]}},"alternative-id":["10.1111\/exsy.70105"],"URL":"https:\/\/2.zoppoz.workers.dev:443\/https\/doi.org\/10.1111\/exsy.70105","archive":["Portico"],"relation":{},"ISSN":["0266-4720","1468-0394"],"issn-type":[{"value":"0266-4720","type":"print"},{"value":"1468-0394","type":"electronic"}],"subject":[],"published":{"date-parts":[[2025,7,24]]},"assertion":[{"value":"2025-03-05","order":0,"name":"received","label":"Received","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-07-12","order":2,"name":"accepted","label":"Accepted","group":{"name":"publication_history","label":"Publication History"}},{"value":"2025-07-24","order":3,"name":"published","label":"Published","group":{"name":"publication_history","label":"Publication History"}}],"article-number":"e70105"}}