Load forecasting is crucial for the operation and planning of electricity generation, transmission, and distribution. In the context of short-term electricity load prediction for residential users, single-task learning methods fail to consider the relationship among multiple residential users and have limited feature extraction capabilities for residential load data. It is challenging to obtain sufficient information from individual residential user load predictions, resulting in poor prediction performance. To address these issues, we propose a framework for multi-task residential short-term load prediction based on contrastive learning. Firstly, clustering techniques are used to select residential users with similar electricity consumption patterns. Secondly, contrastive learning is employed for pre-training, extracting trend and seasonal feature representations of load sequences, thereby enhancing the feature extraction capability for residential load Feature. Lastly, a multi-task learning prediction framework is utilized to learn shared information among multiple residential users' loads, enabling short-term load prediction for multiple residences. The proposed load prediction framework has been implemented on two real-world load data sets, and the experimental results demonstrate that it effectively reduces the prediction errors for residential load prediction. © 2024 Institute of Electrical Engineer of Japan and Wiley Periodicals LLC.

中文翻译：

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基于对比学习的多任务住宅短期负荷预测

负荷预测对于发电、输电和配电的运营和规划至关重要。在住宅用户短期用电负荷预测的背景下，单任务学习方法未能考虑多个住宅用户之间的关系，并且对住宅负荷数据的特征提取能力有限。从单个住宅用户负载预测中获取足够的信息具有挑战性，导致预测性能较差。为了解决这些问题，我们提出了一种基于对比学习的多任务住宅短期负荷预测框架。首先，利用聚类技术选择具有相似用电模式的居民用户。其次，采用对比学习进行预训练，提取负荷序列的趋势和季节特征表示，从而增强住宅负荷特征的特征提取能力。最后，利用多任务学习预测框架来学习多个住宅用户负载之间的共享信息，从而实现多个住宅的短期负载预测。所提出的负荷预测框架已在两个实际负荷数据集上实现，实验结果表明，它有效地减少了住宅负荷预测的预测误差。 © 2024 日本电气工程师协会和 Wiley periodicals LLC。