Interpretable Short-Term Electrical Load Forecasting Scheme Using Cubist

Jihoon Moon, Sungwoo Park, Seungmin Rho, Eenjun Hwang

Research output: Contribution to journalArticlepeer-review

8 Citations (Scopus)


Daily peak load forecasting (DPLF) and total daily load forecasting (TDLF) are essential for optimal power system operation from one day to one week later. This study develops a Cubist-based incremental learning model to perform accurate and interpretable DPLF and TDLF. To this end, we employ time-series cross-validation to effectively reflect recent electrical load trends and patterns when constructing the model. We also analyze variable importance to identify the most crucial factors in the Cubist model. In the experiments, we used two publicly available building datasets and three educational building cluster datasets. The results showed that the proposed model yielded averages of 7.77 and 10.06 in mean absolute percentage error and coefficient of variation of the root mean square error, respectively. We also confirmed that temperature and holiday information are significant external factors, and electrical loads one day and one week ago are significant internal factors.

Original languageEnglish
Article number6892995
JournalComputational Intelligence and Neuroscience
Publication statusPublished - 2022

ASJC Scopus subject areas

  • Computer Science(all)
  • Neuroscience(all)
  • Mathematics(all)


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