轻量模型+智能补全，实现高噪声电网负荷精准预测
A Lightweight DL Model for Smart Grid Power Forecasting with Feature and Resolution Mismatch
- 用小时下采样与双模式补全处理缺失噪声数据
- 轻量GRU-LSTM模型达601.9W均方误差，84.36%准确率
- 适合边缘部署的实时负荷预测场景
当传感器数据存在噪声、不完整且缺乏上下文时，如何实现短期用电量的精确预测？本研究参与了2025年电力能耗预测多指标竞赛，旨在利用真实高频数据预测次日电力需求。我们提出一种鲁棒且轻量的深度学习流程：包括小时级下采样、均值与多项式回归双模式插补，以及综合归一化处理，最终选用标准缩放获得最佳平衡。所设计的轻量GRU-LSTM序列到单值模型平均RMSE为601.9W，MAE为468.9W，准确率达84.36%。尽管输入存在不对称性与插补缺口，模型仍具有良好泛化能力，能捕捉非线性用电规律，并保持低推理延迟。空间时间热图分析显示温度趋势与预测负荷高度一致，进一步验证模型可靠性。结果表明，针对性预处理结合紧凑循环架构可在真实条件下实现快速、准确、可部署的能源预测。
原文摘要 · Abstract (English)
How can short-term energy consumption be accurately forecasted when sensor data is noisy, incomplete, and lacks contextual richness? This question guided our participation in the \textit{2025 Competition on Electric Energy Consumption Forecast Adopting Multi-criteria Performance Metrics}, which challenged teams to predict next-day power demand using real-world high-frequency data. We proposed a robust yet lightweight Deep Learning (DL) pipeline combining hourly downsizing, dual-mode imputation (mean and polynomial regression), and comprehensive normalization, ultimately selecting Standard Scaling for optimal balance. The lightweight GRU-LSTM sequence-to-one model achieves an average RMSE of 601.9~W, MAE of 468.9~W, and 84.36\% accuracy. Despite asymmetric inputs and imputed gaps, it generalized well, captured nonlinear demand patterns, and maintained low inference latency. Notably, spatiotemporal heatmap analysis reveals a strong alignment between temperature trends and predicted consumption, further reinforcing the model's reliability. These results demonstrate that targeted preprocessing paired with compact recurrent architectures can still enable fast, accurate, and deployment-ready energy forecasting in real-world conditions.
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。