Variational Autoencoder-Based Approach to Latent Feature Analysis on Efficient Representation of Power Load Monitoring Data
This provides an incremental data-completion solution for electric load management in smart grids, addressing a domain-specific problem for smart grid operators.
The paper tackles the challenge of high-dimensional and incomplete power load monitoring data for power load forecasting by proposing a variational autoencoder-based model (VAE-LF) that learns low-dimensional latent representations and generates complementary data, achieving lower RMSE and MAE than benchmarks in 5% and 10% sparsity tests on the UK-DALE dataset.
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.