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

Tuukka Salmi, Jussi Kiljander and Daniel Pakkala
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Tuukka Salmi: VTT Technical Research Centre of Finland, FI-02044 Espoo, Finland
Jussi Kiljander: VTT Technical Research Centre of Finland, FI-02044 Espoo, Finland
Daniel Pakkala: VTT Technical Research Centre of Finland, FI-02044 Espoo, Finland

Energies, 2020, vol. 13, issue 9, 1-15

Abstract: 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.

Keywords: deep neural networks; short-term load forecasting (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (4)

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