METHOD AND SYSTEM FOR GRAPH SIGNAL PROCESSING BASED ENERGY MODELLING AND FORECASTING

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.