Predicting Energy Demand with Tensor Factor Models

TL;DR

This paper introduces a tensor factor model for forecasting high-dimensional U.S. electricity demand, capturing multiple seasonal patterns and cross-sectional correlations to improve prediction accuracy and interpretability.

Contribution

The novel tensor-based approach effectively models complex seasonalities in electricity demand data, outperforming traditional methods and providing interpretable factors aligned with domain knowledge.

Findings

Tensor factor model outperforms benchmarks in forecasting accuracy.

The model captures intra-day, intra-week, and cross-provider seasonalities.

Interpretable factors reveal meaningful demand patterns.

Abstract

Hourly consumption from multiple providers displays pronounced intra-day, intra-week, and annual seasonalities, as well as strong cross-sectional correlations. We introduce a novel approach for forecasting high-dimensional U.S. electricity demand data by accounting for multiple seasonal patterns via tensor factor models. To this end, we restructure the hourly electricity demand data into a sequence of weekly tensors. Each weekly tensor is a three-mode array whose dimensions correspond to the hours of the day, the days of the week, and the number of providers. This multi-dimensional representation enables a factor decomposition that distinguishes among the various seasonal patterns along each mode: factor loadings over the hour dimension highlight intra-day cycles, factor loadings over the day dimension capture differences across weekdays and weekends, and factor loadings over the provider dimension reveal commonalities and shared dynamics among the different entities. We rigorously compare the predictive performance of our tensor factor model against several benchmarks, including traditional vector factor models and cutting-edge functional time series methods. The results consistently demonstrate that the tensor-based approach delivers superior forecasting accuracy at different horizons and provides interpretable factors that align with domain knowledge. Beyond its empirical advantages, our framework offers a systematic way to gain insight into the underlying processes that shape electricity demand patterns. In doing so, it paves the way for more nuanced, data-driven decision-making and can be adapted to address similar challenges in other high-dimensional time series applications.