Ansumana Badjan, Ghamgeen Izat Rashed, Hashim Ali I. Gony, Hussain Haider, Ahmed O. M. Bahageel, Husam I. Shaheen
{"title":"Improving short-term wind power forecasting in Senegal’s flagship wind farm: a deep learning approach with attention mechanism","authors":"Ansumana Badjan, Ghamgeen Izat Rashed, Hashim Ali I. Gony, Hussain Haider, Ahmed O. M. Bahageel, Husam I. Shaheen","doi":"10.1007/s00202-024-02681-5","DOIUrl":null,"url":null,"abstract":"<p>Accurate wind power forecasting assumes an important role in power system operation and economic planning, particularly in Senegal’s flagship wind farm, the largest in West Africa. The fundamental volatility, intermittent nature, and unexpected character of wind power make it difficult to maintain power system stability. To address these challenges, an attention mechanism-based deep learning model is proposed to anticipate wind power in the short term with the goal of improving forecasting accuracy. The dynamic shifts in the wind power dataset are first processed by convolutional neural networks to extract multi-dimensional features. After being extracted, the feature vectors are placed into a long short-term memory (LSTM) network by being transformed into a series structure. Next, to optimize and improve the forecast accuracy of the model, an attention mechanism is included by assigning distinct weights to each hidden layer in the LSTM network. Real operational wind power generation data from the wind farm is utilized to verify the effectiveness of the proposed method. The results show that the proposed method can successfully boost the forecasting accuracy of wind power with better performance compared to other machine learning and deep learning models. This study not only contributes to improving wind power generation management and power system operations in Senegal but also serves as a valuable reference for promoting renewable energy transitions across sub-Saharan Africa.</p>","PeriodicalId":50546,"journal":{"name":"Electrical Engineering","volume":null,"pages":null},"PeriodicalIF":1.6000,"publicationDate":"2024-09-09","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Electrical Engineering","FirstCategoryId":"5","ListUrlMain":"https://doi.org/10.1007/s00202-024-02681-5","RegionNum":4,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q3","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
Accurate wind power forecasting assumes an important role in power system operation and economic planning, particularly in Senegal’s flagship wind farm, the largest in West Africa. The fundamental volatility, intermittent nature, and unexpected character of wind power make it difficult to maintain power system stability. To address these challenges, an attention mechanism-based deep learning model is proposed to anticipate wind power in the short term with the goal of improving forecasting accuracy. The dynamic shifts in the wind power dataset are first processed by convolutional neural networks to extract multi-dimensional features. After being extracted, the feature vectors are placed into a long short-term memory (LSTM) network by being transformed into a series structure. Next, to optimize and improve the forecast accuracy of the model, an attention mechanism is included by assigning distinct weights to each hidden layer in the LSTM network. Real operational wind power generation data from the wind farm is utilized to verify the effectiveness of the proposed method. The results show that the proposed method can successfully boost the forecasting accuracy of wind power with better performance compared to other machine learning and deep learning models. This study not only contributes to improving wind power generation management and power system operations in Senegal but also serves as a valuable reference for promoting renewable energy transitions across sub-Saharan Africa.
期刊介绍:
The journal “Electrical Engineering” following the long tradition of Archiv für Elektrotechnik publishes original papers of archival value in electrical engineering with a strong focus on electric power systems, smart grid approaches to power transmission and distribution, power system planning, operation and control, electricity markets, renewable power generation, microgrids, power electronics, electrical machines and drives, electric vehicles, railway electrification systems and electric transportation infrastructures, energy storage in electric power systems and vehicles, high voltage engineering, electromagnetic transients in power networks, lightning protection, electrical safety, electrical insulation systems, apparatus, devices, and components. Manuscripts describing theoretical, computer application and experimental research results are welcomed.
Electrical Engineering - Archiv für Elektrotechnik is published in agreement with Verband der Elektrotechnik Elektronik Informationstechnik eV (VDE).