用变分自编码器补全智能电网负荷数据缺失，提升预测精度。
Variational Autoencoder-Based Approach to Latent Feature Analysis on Efficient Representation of Power Load Monitoring Data
- 基于编码器-解码器结构的VAE模型，将高维不完整负荷数据映射到低维隐空间。
- 在UK-DALE数据集上，5%和10%缺失率下均显著降低RMSE与MAE。
- 适合智能电网中负荷数据缺失场景，尤其对低缺失率数据补全效果更优。
随着智能电网的发展，高维不完整（HDI）电力负荷监测（PLM）数据对电力负荷预测（PLF）模型性能构成挑战。本文提出一种基于变分自编码器（VAE）的潜在特征表征模型VAE-LF，用于高效表示并补全PLM缺失数据。VAE-LF通过将HDI PLM数据拆分为向量并逐个输入模型，利用编码器-解码器结构学习数据的低维隐表示，并生成补全数据。在UK-DALE数据集上的实验表明，无论在5%或10%缺失率测试场景下，VAE-LF均优于其他基准模型，显著降低均方根误差（RMSE）与平均绝对误差（MAE），且在低缺失率条件下表现尤为突出。该方法为智能电网中的电力负荷管理提供了高效的缺失数据补全解决方案。
原文摘要 · Abstract (English)
With the development of smart grids, High-Dimensional and Incomplete (HDI) Power Load Monitoring (PLM) data challenges the performance of Power Load Forecasting (PLF) models. In this paper, we propose a potential characterization model VAE-LF based on Variational Autoencoder (VAE) for efficiently representing and complementing PLM missing data. VAE-LF learns a low-dimensional latent representation of the data using an Encoder-Decoder structure by splitting the HDI PLM data into vectors and feeding them sequentially into the VAE-LF model, and generates the complementary data. Experiments on the UK-DALE dataset show that VAE-LF outperforms other benchmark models in both 5% and 10% sparsity test cases, with significantly lower RMSE and MAE, and especially outperforms on low sparsity ratio data. The method provides an efficient data-completion solution for electric load management in smart grids.
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