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English(EN) Delta-Based Target Reformulation for Short-Term Electricity Load Forecasting Using LSTM and Transformer Models

基于Delta的预测提高了电力负荷预测精度

作者 PulseAugur 编辑部 · [2 个来源] ·

研究人员提出了一种基于Delta的目标重构方法，用于使用LSTM和Transformer等深度学习模型进行短期电力负荷预测。该方法预测连续时间步长之间的负荷变化，而不是绝对负荷，旨在稳定学习目标。使用印度数据的实验表明，这种Delta重构显著提高了提前一小时的预测精度，深度序列模型的MAPE降低了50%以上，但其有效性因模型和预测范围而异。 AI

排序理由 该集群包含一篇学术论文，详细介绍了使用深度学习模型进行电力负荷预测的新方法。[lever_c_demoted from research: ic=1 ai=0.7]

在 arXiv cs.LG 阅读 →

AI 生成摘要 · Google Gemini · 来自 2 个来源。 我们如何撰写摘要 →

报道来源 [2]

  1. arXiv cs.LG TIER_1 English(EN) · Vansh Bansal ·

    Delta-Based Target Reformulation for Short-Term Electricity Load Forecasting Using LSTM and Transformer Models

    arXiv:2606.17692v1 Announce Type: new Abstract: Accurate short-term electricity load forecasting is critical for the reliable and economic operation of modern power systems, under non-stationarity arising from weather variability, calendar effects, and evolving consumption patter…

  2. arXiv cs.LG TIER_1 English(EN) · Vansh Bansal ·

    Delta-Based Target Reformulation for Short-Term Electricity Load Forecasting Using LSTM and Transformer Models

    Accurate short-term electricity load forecasting is critical for the reliable and economic operation of modern power systems, under non-stationarity arising from weather variability, calendar effects, and evolving consumption patterns. While deep learning models such as LSTMs and…

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