用新模型JITtrans提升家庭用电预测精度，助力低碳能源管理。
Just In Time Transformers
- 基于智能电表数据，用Transformer模型按用电行为聚类用户。
- 相比传统方法，预测准确率显著提升，实测验证有效。
- 适合能源公司和政策制定者，推动可持续电力系统发展。
精准的家庭用电负荷预测对减少碳排放、提高能源效率至关重要；准确预测使公用事业公司和政策制定者能够优化资源利用，倡导可持续能源实践。智能电表提供了消费模式的细粒度洞察。基于可用的智能电表数据，本研究旨在根据用电行为将用户聚类为不同群体，有效捕捉多样化的消费模式。随后，我们设计了JITtrans（Just In Time transformer）——一种新型Transformer深度学习模型，显著提升了用电量预测的准确性，优于传统方法。通过自有智能电表数据的大量实验验证了我们的结论。研究结果表明，先进预测技术有望彻底改变能源管理，推动可持续电力系统的发展：高效环保能源解决方案的构建，高度依赖此类技术。
原文摘要 · Abstract (English)
Precise energy load forecasting in residential households is crucial for mitigating carbon emissions and enhancing energy efficiency; indeed, accurate forecasting enables utility companies and policymakers, who advocate sustainable energy practices, to optimize resource utilization. Moreover, smart meters provide valuable information by allowing for granular insights into consumption patterns. Building upon available smart meter data, our study aims to cluster consumers into distinct groups according to their energy usage behaviours, effectively capturing a diverse spectrum of consumption patterns. Next, we design JITtrans (Just In Time transformer), a novel transformer deep learning model that significantly improves energy consumption forecasting accuracy, with respect to traditional forecasting methods. Extensive experimental results validate our claims using proprietary smart meter data. Our findings highlight the potential of advanced predictive technologies to revolutionize energy management and advance sustainable power systems: the development of efficient and eco-friendly energy solutions critically depends on such technologies.
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