Patent ID: 11961028
Assignee: TATA CONSULTANCY SERVICES LIMITED
Field: IT methods for management (Electrical engineering)
Classification: CPC G | IPC G

Claim 0:
1. A processor implemented method of building energy forecasting, the method comprising:
collecting data on energy consumption in a building for a pre-defined time window and a plurality of energy consumption parameters, as input, via one or more hardware processors;
generating a building energy consumption model by processing the plurality of energy consumption parameters via the one or more hardware processors, using a Graph Signal Processing (GSP) modelling, comprising:
constructing a graph for a measured energy value of the building, comprising:
building a weighted adjacent matrix of the GSP using the plurality of energy consumption parameters, wherein the plurality of energy consumption parameters are occupancy of the building, weather information of a region in which the building is located, and selected time of a day, a selected day of a week and holiday information for which forecasting is to be performed;
using samples of the measured energy value as nodes of the graph; and
determining weight of edge between each set of nodes of a graph for each sample of the measured energy value, based on a) vector difference between the plurality of energy consumption parameters, and b) a co-variance matrix of the plurality of energy consumption parameters, wherein the weight of each edge is calculated for a pre-defined time window;

generating a smooth signal by minimizing variation of the graph by performing total variation minimization on the graph, via the one or more hardware processors;
generating forecast using the smooth signal, via the one or more hardware processors, wherein the forecast generated is one of a day-ahead forecast, month-ahead forecast, or a quarter-ahead forecast, wherein generating the one day ahead forecast comprises:
fetching hourly energy consumption data, and the plurality of energy consumption parameters as input;
generating the building energy consumption model using the hourly energy consumption data and the plurality of energy consumption parameters; and
filling a plurality of missing values in the graph using the generated forecast, via the one or more hardware processors.