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

Sandra Wilfling, Masoud Ebrahimi, Qamar Alfalouji, Gerald Schweiger, Mina Basirat

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


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 publication2022 21st IEEE International Conference on Machine Learning and Applications (ICMLA)
EditorsM. Arif Wani, Mehmed Kantardzic, Vasile Palade, Daniel Neagu, Longzhi Yang, Kit-Yan Chan
Number of pages6
ISBN (Electronic)9781665462839
Publication statusPublished - Mar 2023
Event21st IEEE International Conference on Machine Learning and Applications: IEEE ICMLA 2022 - Nassau, Bahamas
Duration: 12 Dec 202214 Dec 2022


Conference21st IEEE International Conference on Machine Learning and Applications
Abbreviated titleICMLA 2022


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

ASJC Scopus subject areas

  • Artificial Intelligence


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