Recurrent Transform Learning
This work addresses building-level energy demand forecasting, which is incremental as it builds on existing forecasting methods with a new technique.
The authors tackled building demand forecasting, a more challenging task than grid-level forecasting, by developing recurrent transform learning (RTL) and its regression-embedded version (R2TL), achieving superior results to state-of-the-art methods like LSTM, echo state network, and sparse coding regression on three public datasets.
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.