Improving short-term wind power forecasting in Senegal’s flagship wind farm: a deep learning approach with attention mechanism

IF 1.6 4区 工程技术 Q3 ENGINEERING, ELECTRICAL & ELECTRONIC
Ansumana Badjan, Ghamgeen Izat Rashed, Hashim Ali I. Gony, Hussain Haider, Ahmed O. M. Bahageel, Husam I. Shaheen
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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.

Abstract Image

改进塞内加尔旗舰风电场的短期风电预测:一种具有注意力机制的深度学习方法
准确的风力发电预测在电力系统运行和经济规划中发挥着重要作用,尤其是在塞内加尔的旗舰风电场(西非最大的风电场)中。风力发电的基本波动性、间歇性和突发性使其难以维持电力系统的稳定性。为了应对这些挑战,我们提出了一种基于注意力机制的深度学习模型,用于预测短期内的风力发电量,以提高预测精度。风电数据集的动态变化首先由卷积神经网络进行处理,以提取多维特征。提取特征后,将特征向量转化为序列结构,并将其放入长短期记忆(LSTM)网络中。接下来,为了优化和提高模型的预测精度,LSTM 网络中的每个隐藏层都分配了不同的权重,从而加入了注意力机制。为了验证所提方法的有效性,我们利用了风电场的真实风力发电运行数据。结果表明,与其他机器学习和深度学习模型相比,所提出的方法能成功提高风力发电预测的准确性,并具有更好的性能。这项研究不仅有助于改善塞内加尔的风力发电管理和电力系统运行,还为促进撒哈拉以南非洲地区的可再生能源转型提供了有价值的参考。
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来源期刊
Electrical Engineering
Electrical Engineering 工程技术-工程:电子与电气
CiteScore
3.60
自引率
16.70%
发文量
0
审稿时长
>12 weeks
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).
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