  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.
