  The objective of this work is to improve the accuracy of building demand
forecasting. This is a more challenging task than grid level forecasting. For
the said purpose, we develop a new technique called recurrent transform
learning (RTL). Two versions are proposed. The first one (RTL) is unsupervised;
this is used as a feature extraction tool that is further fed into a regression
model. The second formulation embeds regression into the RTL framework leading
to regressing recurrent transform learning (R2TL). Forecasting experiments have
been carried out on three popular publicly available datasets. Both of our
proposed techniques yield results superior to the state-of-the-art like long
short term memory network, echo state network and sparse coding regression.
