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AssigneeTata Consultancy Services Limited
InventorsNaveen Kumar Thokala, Spoorthy Paresh, Vishnu Brindavanam, Mariswamy Girish Chandra
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Energy consumption modelling requires to consider various factors affecting the energy consumption in buildings, to be able to effectively forecast future consumption. Even though some of the state of the art deep learning based approaches are able to address these requirements to some extent, they are computationally heavy. The disclosure herein generally relates to energy forecasting, and, more particularly, to a method and system for graph signal processing (GSP) based energy modelling and forecasting. The system monitors and collects information on energy consumption in a building and values of associated energy consumption parameters. This input data is further processed using GSP to generate a building energy consumption model, from which a smooth signal is obtained by applying total variation minimization. The system further performs forecasting using the smooth signal.
A processor implemented method of building energy forecasting, the method comprising:
A system for building energy forecasting, the system comprising:
One or more non-transitory machine-readable information storage mediums comprising one or more instructions which when executed by one or more hardware processors cause:
Dependencies are inferred from the claim text.