US 12,444,973 B2
Energy disaggregation methods and systems using weather data
Mahnameh Taheri, Glasgow (GB); Colin Parry, Glasgow (GB); Alan Wegienka, Glasgow (GB); and Loic Jacob, Glasgow (GB)
Assigned to Arbnco Ltd, Glasgow (GB)
Appl. No. 18/007,465
Filed by Arbnco Ltd, Glasgow (GB)
PCT Filed Jul. 28, 2021, PCT No. PCT/EP2021/071226
§ 371(c)(1), (2) Date Jan. 30, 2023,
PCT Pub. No. WO2022/023455, PCT Pub. Date Feb. 3, 2022.
Claims priority of application No. 2011663 (GB), filed on Jul. 28, 2020.
Prior Publication US 2023/0299611 A1, Sep. 21, 2023
Int. Cl. H02J 13/00 (2006.01)
CPC H02J 13/00001 (2020.01) [H02J 13/00004 (2020.01); H02J 2310/60 (2020.01); H02J 2310/62 (2020.01)] 15 Claims
OG exemplary drawing
 
1. An energy disaggregation system for a building, comprising:
an energy load data interface to receive energy load data originating from energy use sensors for the building;
a weather data interface to receive weather data for a location that includes the building;
a weather adjustment pre-processor to process the energy load data and the weather data and to determine a weather-dependent energy use component of the energy load data and a weather-independent energy use component of the energy load data;
a baseline adjustment pre-processor to process the weather-independent energy use component of the energy load data and determine a baseline energy use component of the energy load data, wherein the baseline adjustment pre-processor is configured to remove the baseline energy use component from the weather-independent energy use component to determine a variable energy use component of the energy load data; and
an energy use disaggregator to process the variable energy use component of the energy load data and determine a plurality of time-varying load components of the energy load data;
the system further comprising:
an occupancy engine to estimate periods of occupancy for the building using the energy load data wherein the energy use disaggregator uses the estimated periods of occupancy to determine the plurality of time-varying load components of the energy load data;
a data partition engine to apply a clustering model to the energy load data to determine one or more partitions within the energy load data; and
a temporal processing engine to segment and aggregate the energy load data over a set of predefined time periods, wherein the temporal processing engine is independently applied to the partitions determined by the data partition engine,
wherein the output of the temporal processing engine is used by the occupancy engine to estimate periods of occupancy for the building;
the system further configured to:
i) determine a set of control actions available within the building that relate to one or more of the plurality of time-varying load components;
ii) predict a change in said one or more of the plurality of time-varying load components in response to one or more of the set of control actions;
iii) output recommendations for control actions that are predicted to reduce the said one or more of the plurality of time-varying load components;
iv) receive a selection of one or more control actions within the recommendations for control actions; and
v) implement the selected one or more control actions within the building;
the one or more control actions comprising at least one of the following:
a) in relation to one or more time-varying load components, switching off or changing one or more operating parameters for one or more appliances and/or items of equipment within the building;
b) in relation to a small power load component, turning off or turning down one or more appliances or items of equipment within the building;
c) in relation to a lighting load component, turning off or turning down a set of lights;
d) in relation to a heating, ventilation and air conditioning (HVAC) load component, changing a desired internal temperature, and/or opening and/or closing ventilation conduits.