A Sparse Linear Model and Significance Test for Individual Consumption Prediction

by   Pan Li, et al.

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 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.



page 21

page 22


Enhancing Explainability of Neural Networks through Architecture Constraints

Prediction accuracy and model explainability are the two most important ...

Results Merging in the Patent Domain

In this paper, we test machine learning methods for results merging in p...

Shape-Based Approach to Household Load Curve Clustering and Prediction

Consumer Demand Response (DR) is an important research and industry prob...

Investigating the Use of One-Class Support Vector Machine for Software Defect Prediction

Early software defect identification is considered an important step tow...

Improved prediction of soil properties with Multi-target Stacked Generalisation on EDXRF spectra

Machine Learning (ML) algorithms have been used for assessing soil quali...

Identifying Hidden Visits from Sparse Call Detail Record Data

Despite a large body of literature on trip inference using call detail r...

Wisdom of the crowd from unsupervised dimension reduction

Wisdom of the crowd, the collective intelligence derived from responses ...
This week in AI

Get the week's most popular data science and artificial intelligence research sent straight to your inbox every Saturday.