Recurrent Transform Learning

2019/12/11 by Megha Gupta, Gupta, Megha, Angshul Majumdar +1
Computer Science · Earth and Planetary Sciences · Engineering · #Energy Load and Power Forecasting #FOS: Computer and information sciences #FOS: Electrical engineering #Image and Signal Denoising Methods #Machine Learning (cs.LG) #Machine Learning (stat.ML) #Meteorological Phenomena and Simulations #Neural and Evolutionary Computing (cs.NE) #Signal Processing (eess.SP) #electronic engineering #information engineering

paper · pdf · doi:10.48550/arxiv.1912.05198

openalex publication_date 2019/12/11 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28

Abstract

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.

Citations

• Short-term load forecasting

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