A Sparse Linear Model and Significance Test for Individual Consumption Prediction

TL;DR

This paper introduces a novel sparse linear modeling approach combined with a significance test to enhance the accuracy of individual energy consumption predictions, accounting for user heterogeneity and leveraging data relationships.

Contribution

It proposes an adaptive sparsity exploration method and a hypothesis testing framework for improved individual consumption prediction accuracy.

Findings

Outperforms support vector machine, PCA with linear regression, and random forest.

Achieves high prediction accuracy with linear computational efficiency.

Validated on PG&E data with extensive simulations.

Abstract

Accurate prediction of user consumption is a key part not only in understanding consumer flexibility and behavior patterns, but in the design of robust and efficient energy saving programs as well. Existing prediction methods usually have high relative errors that can be larger than 30% and have difficulties accounting for heterogeneity between individual users. In this paper, we propose a method to improve prediction accuracy of individual users by adaptively exploring sparsity in historical data and leveraging predictive relationship between different users. Sparsity is captured by popular least absolute shrinkage and selection estimator, while user selection is formulated as an optimal hypothesis testing problem and solved via a covariance test. Using real world data from PG&E, we provide extensive simulation validation of the proposed method against well-known techniques such as support vector machine, principle component analysis combined with linear regression, and random forest. The results demonstrate that our proposed methods are operationally efficient because of linear nature, and achieve optimal prediction performance.