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arXiv AI By Carlos Quesada-Granja, Tony Castillo-Calzadilla, Carlos Rizo-Maestre

Calendar-Structured Sparse Principal Component Analysis for Interpretable Multi-Periodic Electricity Consumption Profiles

Read the original on arXiv AI →

Calendar-Structured Sparse Principal Component Analysis (Calendar-SPCA) is a new method that learns low-dimensional representations of long-term electricity consumption data by explicitly incorporating daily, weekly, and annual calendar cycles. It uses an L1 penalty and graph total variation to produce sparse, locally coherent, and directly interpretable latent factors. In experiments on the GoiEner and Low Carbon London smart‑meter datasets, Calendar-SPCA retains most of the variance of standard PCA while achieving high sparsity and clear calendar‑aligned structures.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv AI.

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