[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-125115-en":3,"doc-seo-125115-105":30,"detail-sidebar-cat-0-en-105":91},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},125115,7971461740886,"Theodore","https://ap-avatar.wpscdn.com/davatar_3d24733baf745e90a7e4bdd5f77d97b2",8,"Research & Report","A comparative analysis of Machine Learning Techniques for short-term grid power forecasting and uncertainty analysis of Wave Energy Converters","Wave energy offers strong renewable potential, yet its power output fluctuates, making accurate forecasting essential for grid-quality compliance and for advanced smart-grid monitoring and control. The study introduces a short-term (15–240 min) forecasting methodology for the Inertial Sea Wave Energy Converter (ISWEC), which uses a gyroscope inertial effect. Five time-series models are designed, optimized, compared, and evaluated under multiple downsampling schemes, and prediction intervals are built via a non-parametric kernel density estimator to quantify uncertainty.","POLITECNICO DI TORINO  \nRepository ISTITUZIONALE  \nA comparative analysis of Machine Learning Techniques for short-term grid power forecasting and uncertainty analysis of Wave Energy Converters  \nOriginal  \nA comparative analysis of Machine Learning Techniques for short-term grid power forecasting and uncertainty analysis of Wave Energy Converters / FONTANA CRESPO, RAFAEL NATALIO; Aliberti, Alessandro; Bottaccioli, Lorenzo; Pasta, Edoardo; Sirigu, SERGEJ ANTONELLO; Macii, Enrico; Mattiazzo, Giuliana; Patti, Edoardo. -In: ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE. -ISSN 0952-1976. -138, Part A:(2024) .  \n[10.1016/j.engappai.2024.109352]  \nAvailability:  \nThis version is available at: 11583/2992828 since: 2024-09-26T16:31:29Z  \nPublisher: Elsevier  \nPublished  \nDOI:10.1016/j.engappai.2024.109352  \nTerms of use:  \nThis article is made available under terms and conditions as specified in the corresponding bibliographic description in the repository  \nPublisher copyright  \n(Article begins on next page)  \n17 February 2025  \nEngineering Applications of Artiϧcial Intelligence 138 (2024) 109352  \n| Research paper\u003Cbr>A comparative analysis of Machine Learning Techniques for short-term grid power forecasting and uncertainty analysis of Wave Energy Converters Rafael Natalio Fontana Crespo a,∗, Alessandro Alibertia, Lorenzo Bottacciolib, Edoardo Pastac, Sergej Antonello Sirigu c, Enrico Maciia, Giuliana Mattiazzo c, Edoardo Patti a,b\u003Cbr>a Department of Control and Computer Engineering, Politecnico di Torino, 10129, Turin, Italy b Energy Center Lab, Politecnico di Torino, 10129, Turin, Italy\u003Cbr>c Department of Mechanical and Aerospace Engineering, Politecnico di Torino, 10129, Turin, Italy |  |  |  |\n| --- | --- | --- | --- |\n| A R T I C L E I N F O |  | A B S T R A C T |  |\n| Keywords:\u003Cbr>Renewable energy Wave energy converter Energy forecast Neural networks\u003Cbr>Non-parametric kernel density estimation Uncertainty analysis\u003Cbr>Prediction intervals |  | Wave Energy is one of the renewable sources with greatest potential. Since power coming from waves fluctuates, the grid integration of wave energy involves several power conditioning stages to comply with grid quality requirements. However, to ensure full integration of wave energy in a smart grid scenario and unlock advanced monitoring and control techniques (e.g. Demand/Response), it is crucial to forecast the output power. This work proposes a methodology to forecast in short-term horizons (i.e. 15 min to 240 min) the power delivered to the grid of the Inertial Sea Wave Energy Converter (ISWEC), a device that harnesses wave power through the inertial effect of a gyroscope. Therefore, we designed, optimized and compared the performance of five known machine learning techniques for time series point forecasting: Random Forest, Support Vector Regression, Long Short-Term Memory Neural Network, Transformer Neural Network and 1 Dimensional Convolutional Neural Network. Additionally, we studied the efficacy of downsampling technique aggregating original dataset sampled every 0.1 s in time steps of 1min, 3min, 5min and 15min to compare the performance behaviour of the different machine learning models for these datasets. Furthermore, we implemented Prediction Intervals (PIs) to calculate the inherent uncertainties associated with the previously mentioned machine learning techniques. These PIs were built based on the Non-Parametric Kernel Density Estimator technique. The point forecasting and the PIs results showed that models’ performance improved as the downsampling increased. Moreover, the Random Forest model was the worst-performing in all cases. Finally, none of the other models can be considered the best overall. |  |\n\n1. Introduction  \nNowadays, Renewable Energy Sources (RES) are a very hot topic, attracting more and more attention. Implementing environmentally friendly energy alternatives to either replace or drastically reduce the usage of fossil fuels has become an essential obje","cbCaiedahuR3jv6Y","https://ap.wps.com/l/cbCaiedahuR3jv6Y","pdf",6781108,1,37,"English","en",105,"# Introduction\n## Problem context: renewable integration and forecasting needs\n## Wave Energy Converters and ISWEC overview\n# Methodology\n## Machine learning models for point forecasting\n## Downsampling strategy and evaluation\n## Prediction intervals for uncertainty quantification\n# Results and discussion","[{\"question\":\"What forecasting horizon does the methodology target for wave power?\",\"answer\":\"It forecasts short-term power delivered to the grid over horizons from 15 minutes to 240 minutes.\"},{\"question\":\"Which machine learning models are compared in the study?\",\"answer\":\"The study compares Random Forest, Support Vector Regression, Long Short-Term Memory, Transformer neural networks, and 1D Convolutional Neural Networks.\"},{\"question\":\"How are uncertainties quantified in the paper?\",\"answer\":\"Uncertainty is quantified using prediction intervals constructed with a non-parametric kernel density estimator approach.\"}]","A comparative analysis of Machine Learning Techniques for short-term grid power forecasting and uncertainty analysis of Wave Energy Converters | 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forecasting horizon does the methodology target for wave power?","Question",{"text":75,"@type":76},"It forecasts short-term power delivered to the grid over horizons from 15 minutes to 240 minutes.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which machine learning models are compared in the study?",{"text":80,"@type":76},"The study compares Random Forest, Support Vector Regression, Long Short-Term Memory, Transformer neural networks, and 1D Convolutional Neural Networks.",{"name":82,"@type":73,"acceptedAnswer":83},"How are uncertainties quantified in the paper?",{"text":84,"@type":76},"Uncertainty is quantified using prediction intervals constructed with a non-parametric kernel density estimator 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