Stacked Boosters Network Architecture for Short-Term Load Forecasting in Buildings

Tuukka Salmi (Corresponding Author), Jussi Kiljander, Daniel Pakkala

Research output: Contribution to journalArticleScientificpeer-review

11 Citations (Scopus)


This paper presents a novel deep learning architecture for short-term load forecasting of building energy loads. The architecture is based on a simple base learner and multiple boosting systems that are modelled as a single deep neural network. The architecture transforms the original multivariate time series into multiple cascading univariate time series. Together with sparse interactions, parameter sharing and equivariant representations, this approach makes it possible to combat against overfitting while still achieving good presentation power with a deep network architecture. The architecture is evaluated in several short-term load forecasting tasks with energy data from an office building in Finland. The proposed architecture outperforms state-of-the-art load forecasting model in all the tasks.
Original languageEnglish
Article number2370
Issue number9
Publication statusPublished - 9 May 2020
MoE publication typeA1 Journal article-refereed


  • deep neural networks
  • short-term load forecasting


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