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Accuracy is evaluated across different validation and modeling approaches, including hybrid techniques and comparisons with real-time data. The work delivers guidance on turbine placement and configuration to optimize renewable energy use.",{"@graph":14,"@context":73},[15,34,56],{"@type":16,"itemListElement":17},"BreadcrumbList",[18,23,27,31],{"item":19,"name":20,"@type":21,"position":22},"https://docshare.wps.com","Home","ListItem",1,{"item":24,"name":25,"@type":21,"position":26},"https://docshare.wps.com/document/","Document",2,{"item":28,"name":29,"@type":21,"position":30},"https://docshare.wps.com/document/research-report/","Research & 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is the main goal of the study?","Question",{"text":63,"@type":64},"The study validates machine learning models to predict turbine energy output for different wind conditions and assesses their accuracy.","Answer",{"name":66,"@type":61,"acceptedAnswer":67},"How are the ML models built and tested in this research?",{"text":68,"@type":64},"Models are trained using wind speed, turbine parameters (such as blade length and tower height), and energy-output data, then tested across different validation and hybrid modeling approaches with real-time data comparisons.",{"name":70,"@type":61,"acceptedAnswer":71},"What conclusions and recommendations does the research provide?",{"text":72,"@type":64},"Based on the findings, it recommends practical approaches for placing and configuring wind turbines in different regions to optimize renewable energy utilization and 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(xxxx-xxxx)  \nThe 5th International Conference on Energy and Power, ICEP2024  \n4-6 December 2024 in Bali, Indonesia  \nAssessment of machine learning models for prediction of turbine energy output for different wind conditions  \nKishu Sainia, Amaan Khanb, A. Rathan Babuc, Jose I. Rojasd, J. Madhuri ea,dDepartment of Physics-Division of Aerospace Engineering,  \nUniversitat Politécnica de Catalunya, c/ Esteve Terradas, 7, 08860 Castelldefels, Spain  \nb,c,eDepartment ofAeronautical Engineering, Institute of Aeronautical Engineering, Dundigal, Hyderabad,  \nTelangana-500043, India,  \n[Corresponding Author Email:](Corresponding Author Email: a.rathanbabu@iare.ac.in)[ a.rathanbabu@iare.ac.in](Corresponding Author Email: a.rathanbabu@iare.ac.in)  \nCONTEXT  \nAs global challenges such as climate change, resource depletion, and environmental degradation intensify, the need for sustainable solutions has never been more urgent. AI technologies such as machine learning, neural networks, and predictive analytics are being deployed to tackle complex sustainability challenges, from optimizing energy consumption in smart cities to enhancing agricultural productivity in the face of climate variability.  \nPURPOSE OR GOAL  \nThis research explores how machine learning (ML) methods are being harnessed for sustainability projects, emphasising the crucial role that engineers and technologists play in this transformative process. As an initial step of work, validation of ML models is performed by training the models with wind, turbine, and energy output data. The accuracy of models is tested and detailed in this work.  \nDESIGN/APPROACH OR METHODOLOGY/METHODS  \nThis research employs a rigorous study on different ML model validation methods and modeling approaches using hybrid techniques, then comparing them to real time data. Models are trained with wind speed, turbine parameters such as blade length, height of the tower, diameter of hub, and energy output.  \nFINDINGS: ACTUAL OR ANTICIPATED OUTCOMES  \nAnticipated outcomes encompass comprehensive insights into different ML models and comparing them with hybrid models. Realistic ML models and hybrid models will be found for prediction of future wind data and energy output with different turbine parameters.  \nCONCLUSIONS/RECOMMENDATIONS  \nDrawing upon the findings, the study offers practical recommendations for the placement and configuration of wind turbines in various regions, aiming to optimize renewable energy utilization and sustainability.  \nKEYWORDS  \nWind speed, Blade length, Tower height, Energy output, and Machine Learning models.  \nPAPER TYPE  \nOriginal research.  \nREFERENCES (OPTIONAL)  \n1. Arias, Santiago, Jose I. Rojas, Rathan B. Athota, and Adeline Montlaur. 2023. \"Simulations of Wind Formation in Idealised Mountain–Valley Systems Using OpenFOAM\" Sustainability 15 , no. 2: 1387.  \n[https://doi.org/10.3390/su15021387](https://doi.org/10.3390/su15021387) .  \n2. Patterson, D. , Gonzalez, J. , Le, Q. V. , Liang, C. , Munguia, L. M. , Rothchild, D. , & Dean, J. (2021) . Carbon emissions and large neural network training. arXiv preprint arXiv:2104.10350.  \n[https://doi.org/10.48550/arXiv.2104.10350](https://doi.org/10.48550/arXiv.2104.10350)  \nNOTE: The Abstract should be no more than 1 page. Send this abstract [to](to icep2024@asep.org.au)[ ](to icep2024@asep.org.au)[icep2024@asep.org.au](to icep2024@asep.org.au)","cbCaivCe8O1QA16O","https://ap.wps.com/l/cbCaivCe8O1QA16O","pdf",377293,"English","# Context\n# Purpose or Goal\n# Design/Approach or Methodology/Methods\n# Findings: Actual or Anticipated Outcomes\n# Conclusions/Recommendations\n# Keywords","[{\"question\":\"What is the main goal of the study?\",\"answer\":\"The study validates machine learning models to predict turbine energy output for different wind conditions and assesses their accuracy.\"},{\"question\":\"How are the ML models built and tested in this research?\",\"answer\":\"Models are trained using wind speed, turbine parameters (such as blade length and tower height), and energy-output data, then tested across different validation and hybrid modeling approaches with real-time data comparisons.\"},{\"question\":\"What conclusions and recommendations does the research provide?\",\"answer\":\"Based on the findings, it recommends practical approaches for placing and configuring wind turbines in different regions to optimize renewable energy utilization and sustainability.\"}]","Assessment of machine learning models for prediction of turbine energy output for different wind conditions | PDF"]