Fast and Accurate Power Load Data Completion via Regularization-optimized Low-Rank Factorization

Low - rank representat i on learn ing ha s emerged as a powerful tool for recoverin g missing values i n power load data due to its ability to exploit the inherent low - dimensional structures of spatiotemporal measurements. Among various techniques, low - rank factorization models are f a vou red f o r t he ir efficiency and interpretability . Howeve r, their performan ce is highly sensitive to the choice of regularization parameter s, which are typically fixed or manually tuned, resulting in limited generalization capability or slow convergenc e in pra ctica l sc en arios. In this paper, we propo se a Regular ization - optimized Low - Rank Factorization, which introduces a Proportional - Integral - Derivative controller to adaptively adju st the regularization coefficient . Furthe rmore, we provide a detailed algori t hmi c com plex i t y analysis, showing that our method preser ves the computatio nal efficiency of stochastic gradient descent while improving ad aptivity. Experimental results on real - world power load datasets validate the superiority of our method in both imput a tio n acc urac y and training efficiency compared to existi ng baselines.