Enhancing electric vehicle charging load prediction in data-scarce scenarios: A hybrid deep learning-based approach integrating clustering analysis and transfer learning

IF 9.6 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE
Rehman Zafar , Pei Huang , Yongjun Sun
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引用次数: 0

Abstract

Accurate electric vehicle (EV) load forecasting is crucial for efficient grid operations and demand-side management, yet it is challenging in data-scarce scenarios. Transfer learning (TL) offers a solution by transferring knowledge from data-rich to data-limited scenarios. However, when the knowledge domain exhibits highly diverse behaviors, applying TL alone could introduce large biases, reducing accuracy and limiting its effectiveness. To address this problem, this study proposes a hybrid deep learning-based framework that integrates TL and K-means clustering. The proposed approach consists of two phases. In the source domain phase, a deep-learning-based model is trained using the full dataset and then fine-tuned using clustered user behaviors. In the target domain phase with limited data, TL is applied to transfer knowledge from the source-domain fine-tuned cluster models. For validation, the developed prediction method has been tested using real-world datasets and compared with two other cases: one with applying TL from the source-domain base model trained from full dataset, and one without applying TL. Results show the hybrid method improves forecasting accuracy, reducing the normalized root mean squared error by 3.99 % and 8.22 %, respectively. This study establishes a structured approach for targeted knowledge transfer, enhancing prediction accuracy in data-scarce settings. The framework is scalable and adaptable to other energy forecasting applications, supporting sustainable and resilient energy management.

Abstract Image

增强数据稀缺场景下的电动汽车充电负荷预测:一种融合聚类分析和迁移学习的混合深度学习方法
准确的电动汽车(EV)负荷预测对于有效的电网运行和需求侧管理至关重要,但在数据稀缺的情况下具有挑战性。迁移学习(TL)提供了一种解决方案,将知识从数据丰富的场景转移到数据有限的场景。然而,当知识领域表现出高度多样化的行为时,单独使用TL可能会引入很大的偏差,降低准确性并限制其有效性。为了解决这个问题,本研究提出了一个基于深度学习的混合框架,该框架集成了TL和K-means聚类。建议的方法包括两个阶段。在源域阶段,使用完整数据集训练基于深度学习的模型,然后使用聚类用户行为进行微调。在数据有限的目标领域阶段,应用TL从源领域微调聚类模型中转移知识。为了验证所开发的预测方法,使用真实数据集进行了测试,并与另外两种情况进行了比较:一种是使用完整数据集训练的源域基础模型的TL,另一种是不使用TL。结果表明,混合方法提高了预测精度,将归一化均方根误差分别降低了3.99%和8.22%。本研究建立了一种结构化的定向知识转移方法,提高了数据稀缺环境下的预测准确性。该框架可扩展并适用于其他能源预测应用,支持可持续和弹性能源管理。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
Energy and AI
Energy and AI Engineering-Engineering (miscellaneous)
CiteScore
16.50
自引率
0.00%
发文量
64
审稿时长
56 days
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