{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,11]],"date-time":"2026-03-11T09:49:36Z","timestamp":1773222576669,"version":"3.50.1"},"reference-count":57,"publisher":"Wiley","license":[{"start":{"date-parts":[[2022,6,6]],"date-time":"2022-06-06T00:00:00Z","timestamp":1654473600000},"content-version":"unspecified","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Journal of Electrical and Computer Engineering"],"published-print":{"date-parts":[[2022,6,6]]},"abstract":"<jats:p>Ensuring the supply of electricity in a reliable and safe way is not an easy task, especially when considering renewable and clean energy generated with wind turbines given the intermittency or variability of the wind; also considering different time horizons increases complexity. Mexico has great potential for wind energy in the Eastern region and, to meet this challenge, a platform capable of generating forecast models automatically through mathematical techniques and artificial intelligence and managing them is proposed aimed at providing support based on knowledge and presenting the information graphically through a flexible dashboard, which is customizable and accessible when and where required. In this investigation, components related to the generation of electrical energy in this area are identified and a centralized system is proposed, with information segmentation, management of 3 user profiles, 6 KPIs, 5 configurable parameters, 7 different forecast models using statistical techniques, support vector machines, and automatic and deep learning, with 2 ways of visualization, to carry out analyses at 3 different time horizons. It is built in a modular way with free and open-source software. The results in the energy sector show that it allows focusing on priority activities avoiding rework, ensures reliability and completeness, is scalable, avoids duplication, allows resources to be shared, responds quickly to hypotheses, and has a global and summarized view of relevant data according to the interested party for different periods of time in an agile way, reducing times and offering support to the decision maker.<\/jats:p>","DOI":"10.1155\/2022\/5193336","type":"journal-article","created":{"date-parts":[[2022,6,6]],"date-time":"2022-06-06T20:50:08Z","timestamp":1654548608000},"page":"1-12","source":"Crossref","is-referenced-by-count":1,"title":["Analytical, Dynamic, and Interactive Platform for Generation and Managing Predictive Models Focused on Energy Sector"],"prefix":"10.1155","volume":"2022","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-9535-5423","authenticated-orcid":true,"given":"In\u00e9s","family":"Romero","sequence":"first","affiliation":[{"name":"Programa de Desarrollo Sostenible y Energ\u00edas Renovables, Universidad Internacional Iberoamericana, Campeche 24560, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-9183-6086","authenticated-orcid":true,"given":"Alberto","family":"Ochoa-Zezzati","sequence":"additional","affiliation":[{"name":"Departamento\/Facultad: Inteligencia Artificial Aplicada, Universidad Aut\u00f3noma de Ciudad Ju\u00e1rez, Chihuahua 32310, Mexico"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"311","reference":[{"key":"1","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-63913-0"},{"key":"2","volume-title":"Data Mining: Concepts and Techniques","author":"J. Pei","year":"2021"},{"key":"3","volume-title":"Distributed Systems Architecture: A Middleware Approach","author":"A. Puder","year":"2005"},{"key":"4","volume-title":"Demand-Driven Forecasting","author":"C. Chase","year":"2009"},{"key":"5","article-title":"En M\u00e9xico se tiene capacidad instalada para generar electricidad a trav\u00e9s de energ\u00eda renovable en 31 por ciento","author":"R. Nahle","year":"2020"},{"key":"6","doi-asserted-by":"crossref","DOI":"10.1007\/978-1-4419-0925-1","volume-title":"Bayesian and Frequentist Regression Methods","author":"J. Wakefield","year":"2013"},{"key":"7","volume-title":"Modeling and Forecasting Electricity Loads and Prices: A Statistical Approach","author":"R. Weron","year":"2007"},{"key":"8","doi-asserted-by":"publisher","DOI":"10.5772\/48657"},{"key":"9","doi-asserted-by":"publisher","DOI":"10.1109\/59.544636"},{"key":"10","doi-asserted-by":"publisher","DOI":"10.1214\/aos\/1176347963"},{"key":"11","volume-title":"Predicci\u00f3n de demanda de energ\u00eda en Colombia mediante un sistema de inferencia difuso neuronal","author":"A. Garc\u00eda","year":"2005"},{"key":"12","doi-asserted-by":"publisher","DOI":"10.4324\/9781849774918"},{"key":"13","article-title":"JavaScript Gallery Overview","author":"J. S. Canvas","year":"2021"},{"key":"14","article-title":"The Web Framework for Perfectionists with Deadlines | Django","author":"S. F. Django","year":"2021"},{"key":"15","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-58487-4"},{"key":"16","doi-asserted-by":"publisher","DOI":"10.1002\/9780470755624"},{"key":"17","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-50017-1"},{"key":"18","volume-title":"Business Intelligence: T\u00e9cnicas, herramientas y aplicaciones. Espa\u00f1a, Alfaomega","author":"M. P\u00e9rez","year":"2015"},{"key":"19","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-14142-8"},{"key":"20","volume-title":"Data Mining: Introductory and Advanced Topics","author":"M. H. Dunham","year":"2002"},{"key":"21","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-319-55444-0"},{"key":"22","volume-title":"The Elements of Statistical Learning Data Mining, Inference, and Prediction","author":"H. Trevor","year":"2001"},{"key":"23","article-title":"Explore energy data by category, indicator, country or region","author":"International Energy Agency (Iea)","year":"2021"},{"key":"24","article-title":"Pron\u00f3stico de la demanda el\u00e9ctrica residencial basado en el modelo de regresi\u00f3n adaptativa mulltivariante spline (MARS)","author":"M. E. Ortiz"},{"key":"25","article-title":"Programa de desarrollo del sistema el\u00e9ctrico nacional","author":"Secretar\u00eda de Energ\u00eda (Sener)","year":"2021"},{"key":"26","article-title":"Capacidad instalada y generaci\u00f3n de electricidad por sector y fuente de energ\u00eda limpia","author":"Secretar\u00eda de Medio Ambiente y Recursos Naturales (SEMARNAT)","year":"2021"},{"key":"27","volume-title":"The Architecture of Computer Hardware, Systems Software, and Networking: An Information Technology Approach","author":"I. Englander","year":"2014"},{"key":"28","volume-title":"Business Intelligence and Analytics: El arte de convertir los datos en conocimiento","author":"S. Ramos","year":"2016"},{"key":"29","volume-title":"Big Book of Dashboards: Visualizing Your Data Using Real-World Business Scenarios","author":"J. Shaffer","year":"2017"},{"key":"30","article-title":"Estimaci\u00f3n de la Demanda Real","author":"Centro Nacional de Control de Energ\u00eda (Cenace)","year":"2021"},{"key":"31","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4419-6123-5"},{"key":"32","article-title":"Extracci\u00f3n de conocimiento en grandes bases de datos utilizando estrategias adaptativas","author":"W. Hasperu\u00e9","year":"2012"},{"key":"33","doi-asserted-by":"publisher","DOI":"10.1007\/0-387-25465-x_1"},{"key":"34","article-title":"How Artificial Intelligence Will Revolutionize the Energy Industry. Special Edition on Artificial Intelligence","author":"F. Wolfe","year":"2017"},{"key":"35","doi-asserted-by":"publisher","DOI":"10.1007\/978-3-658-11039-0"},{"key":"36","volume-title":"Introduction to Time Series and Forecasting","author":"R. A. Davis","year":"2016"},{"key":"37","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-319-74953-2","volume-title":"Time Predictions: Understanding and Avoiding Unrealism in Project Planning and Everyday Life","author":"T. Halkjelsvik","year":"2018"},{"key":"38","article-title":"Modelo de promedios m\u00f3viles para el pron\u00f3stico horario de potencia y energ\u00eda el\u00e9ctrica. El Hombre y la M\u00e1quina","author":"D. Valencia","year":"2007"},{"key":"39","volume-title":"Forecasting: Principles and Practice","author":"R. J. Hyndman","year":"2018"},{"key":"40","doi-asserted-by":"publisher","DOI":"10.22517\/23447214.7379"},{"key":"41","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4614-6849-3"},{"issue":"167","key":"42","article-title":"Electricidad usando modelos No lineales recent advances in load forecasting using nonlinear models","volume":"78","author":"V. M. Rueda","year":"2015","journal-title":"Dyna"},{"key":"43","volume-title":"Business Intelligence and Data Mining","author":"A. K. Maheshwari","year":"2015"},{"key":"44","volume-title":"Introduction to Time Series Analysis and Forecasting (Wiley Series in Probability and Statistics)","author":"D. C. Montgomery","year":"2008"},{"key":"45","doi-asserted-by":"publisher","DOI":"10.1007\/978-1-4471-5201-9"},{"key":"46","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-319-15195-3","volume-title":"Machine Learning in Medicine - a Complete Overview","author":"T. J. Cleophas","year":"2015"},{"key":"47","volume-title":"Inteligencia Artificial Y Sistemas Expertos","author":"L. Hidalgo","year":"1997"},{"key":"48","doi-asserted-by":"crossref","DOI":"10.1007\/978-3-319-73004-2","volume-title":"Introduction to Deep Learning: From Logical Calculus to Artificial Intelligence","author":"S. Skansi","year":"2018"},{"key":"49","article-title":"Dive into Deep Learning","author":"A. Zhang","year":"2021"},{"key":"50","doi-asserted-by":"publisher","DOI":"10.1007\/s11740-021-01081-z"},{"key":"51","volume-title":"Real-time Big Data Analytics: Emerging Architecture","author":"M. Barlow","year":"2013"},{"key":"52","doi-asserted-by":"crossref","DOI":"10.1007\/978-1-4471-4884-5","volume-title":"Principles of Data Mining","author":"M. Bramer","year":"2013","edition":"2a"},{"key":"53","doi-asserted-by":"publisher","DOI":"10.1016\/j.bdr.2015.03.003"},{"key":"54","volume-title":"The Elements of Statistical Learning: Data Mining, Inference, and Prediction","author":"J. Friedman","year":"2009"},{"key":"55","volume-title":"Climate Change and Energy","author":"American Petroleum Institute (Api)","year":"2021"},{"key":"56","doi-asserted-by":"publisher","DOI":"10.17226\/14673"},{"key":"57","doi-asserted-by":"publisher","DOI":"10.3390\/su132313261"}],"container-title":["Journal of Electrical and Computer Engineering"],"original-title":[],"language":"en","link":[{"URL":"http:\/\/downloads.hindawi.com\/journals\/jece\/2022\/5193336.pdf","content-type":"application\/pdf","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/jece\/2022\/5193336.xml","content-type":"application\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"http:\/\/downloads.hindawi.com\/journals\/jece\/2022\/5193336.pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2022,6,6]],"date-time":"2022-06-06T20:50:34Z","timestamp":1654548634000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.hindawi.com\/journals\/jece\/2022\/5193336\/"}},"subtitle":[],"editor":[{"given":"Antonio","family":"Bracale","sequence":"additional","affiliation":[],"role":[{"role":"editor","vocabulary":"crossref"}]}],"short-title":[],"issued":{"date-parts":[[2022,6,6]]},"references-count":57,"alternative-id":["5193336","5193336"],"URL":"https:\/\/doi.org\/10.1155\/2022\/5193336","relation":{},"ISSN":["2090-0155","2090-0147"],"issn-type":[{"value":"2090-0155","type":"electronic"},{"value":"2090-0147","type":"print"}],"subject":[],"published":{"date-parts":[[2022,6,6]]}}}