{"title":"A Survey of Artificial Intelligence Applications in Wind Energy Forecasting","authors":"Poonam Dhaka, Mini Sreejeth, M. M. Tripathi","doi":"10.1007/s11831-024-10182-8","DOIUrl":null,"url":null,"abstract":"<p>Renewable energy forecasting, such as Wind and Solar forecasting, is becoming more critical as the demand for clean energy increases. Thus, it is crucial to enhance the accuracy of wind power predictions to ensure electrical energy system’s efficient, reliable, and safe operation. Research on wind forecasting has increased dramatically over the past 10 years due to the success of Artificial Intelligence (AI) technologies like machine learning and deep learning. Despite their potential, AI approaches are fraught with uncertainties. It remains unclear how certain factors may influence the accuracy of AI algorithm predictions. This study reviews AI applications in Wind energy forecasting, aiming to provide an analysis of (1) AI-based structures and optimizers for Wind forecasting, (2) forecast performance evaluation for Deterministic and Probabilistic techniques.</p>","PeriodicalId":55473,"journal":{"name":"Archives of Computational Methods in Engineering","volume":null,"pages":null},"PeriodicalIF":9.7000,"publicationDate":"2024-09-13","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Archives of Computational Methods in Engineering","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1007/s11831-024-10182-8","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"COMPUTER SCIENCE, INTERDISCIPLINARY APPLICATIONS","Score":null,"Total":0}
引用次数: 0
Abstract
Renewable energy forecasting, such as Wind and Solar forecasting, is becoming more critical as the demand for clean energy increases. Thus, it is crucial to enhance the accuracy of wind power predictions to ensure electrical energy system’s efficient, reliable, and safe operation. Research on wind forecasting has increased dramatically over the past 10 years due to the success of Artificial Intelligence (AI) technologies like machine learning and deep learning. Despite their potential, AI approaches are fraught with uncertainties. It remains unclear how certain factors may influence the accuracy of AI algorithm predictions. This study reviews AI applications in Wind energy forecasting, aiming to provide an analysis of (1) AI-based structures and optimizers for Wind forecasting, (2) forecast performance evaluation for Deterministic and Probabilistic techniques.
期刊介绍:
Archives of Computational Methods in Engineering
Aim and Scope:
Archives of Computational Methods in Engineering serves as an active forum for disseminating research and advanced practices in computational engineering, particularly focusing on mechanics and related fields. The journal emphasizes extended state-of-the-art reviews in selected areas, a unique feature of its publication.
Review Format:
Reviews published in the journal offer:
A survey of current literature
Critical exposition of topics in their full complexity
By organizing the information in this manner, readers can quickly grasp the focus, coverage, and unique features of the Archives of Computational Methods in Engineering.