Learning Non-linear White-box Predictors: A Use Case in Energy Systems

Research output: Chapter in Book/Report/Conference proceedingConference paperpeer-review

Abstract

Many applications in energy systems require models that represent the non-linear dynamics of the underlying systems. Black-box models with non-linear architecture are suitable candidates for modeling these systems; however, they
are computationally expensive and lack interpretability. An inexpensive white-box linear combination learned over a suitable polynomial feature set can result in a high-performing non-linear model that is easier to interpret, validate, and verify against reference models created by the domain experts. This
paper proposes a workflow to learn a linear combination of non-linear terms for an engineered polynomial feature set. We firstly detect non-linear dependencies and then attempt to reconstruct them using feature expansion. Afterwards, we select possible predictors with the highest correlation coefficients for predictive
regression analysis. We demonstrate how to learn inexpensive yet comprehensible linear combinations of non-linear terms from four datasets. Experimental evaluations show our workflow yields improvements in the metrics R2, CV-RMSE and MAPE in all datasets. Further evaluation of the learned models’ goodness of fit using prediction error plots also confirms that the proposed
workflow results in models that can more accurately capture the nature of the underlying physical systems.
Original languageEnglish
Title of host publicationProceedings 21st IEEE International Conference on Machine Learning and Applications (ICMLA)
Pages507-512
DOIs
Publication statusAccepted/In press - 2022
Event21st IEEE International Conference on Machine Learning and Applications: IEEE ICMLA 2022 - Nassau, Bahamas
Duration: 12 Dec 202214 Dec 2022

Conference

Conference21st IEEE International Conference on Machine Learning and Applications
Abbreviated titleICMLA 2022
Country/TerritoryBahamas
CityNassau
Period12/12/2214/12/22

Keywords

  • Machine Learning
  • data-driven modeling
  • Energy Consumption Prediction
  • Feature Engineering
  • Polynomial Expansion

ASJC Scopus subject areas

  • Artificial Intelligence

Fingerprint

Dive into the research topics of 'Learning Non-linear White-box Predictors: A Use Case in Energy Systems'. Together they form a unique fingerprint.

Cite this