SYSTEM AND METHOD FOR DETERMINING DEMAND SHEDDING EVENTS FOR ENERGY MANAGEMENT
A method including determining, via a machine learning model, a predicted energy load profile for a facility based at least in part on weather forecast data and sensor data for the facility. The sensor data can be received from one or more energy monitoring sensors for one or more devices in the facility. The method further can include determining one or more demand shedding time slots based at least in part on peak periods and the predicted energy load profile. Moreover, the method can include determining one or more demand shedding events for the one or more devices to be scheduled during the one or more demand shedding time slots. The method additionally can include causing a respective performance of each of the one or more demand shedding events by the one or more devices during the one or more demand shedding time slots. Other embodiments are disclosed.
1 . A system comprising:
one or more processors; and
one or more non-transitory computer-readable media storing computing instructions that, when run on the one or more processors, cause the one or more processors to perform:
determining, via a machine learning model, a predicted energy load profile for a facility based at least in part on weather forecast data and sensor data for the facility, wherein:
the sensor data are received from one or more energy monitoring sensors for one or more devices in the facility;
determining one or more demand shedding time slots based at least in part on peak periods and the predicted energy load profile;
determining one or more demand shedding events for the one or more devices to be scheduled during the one or more demand shedding time slots; and
causing a respective performance of each of the one or more demand shedding events by the one or more devices during the one or more demand shedding time slots.
2 . The system in claim 1 , wherein:
the machine learning model comprises an ensemble of a long-term predicting module and a short-term predicting module.
3 . The system in claim 2 , wherein determining, via the machine learning model, the predicted energy load profile further comprises:
determining, via the long-term predicting module, a first portion of the predicted energy load profile based at least in part on the weather forecast data and the sensor data for the facility; and
after determining the first portion, determining, via the short-term predicting module, a remaining portion of the predicted energy load profile based on residuals from the long-term predicting module.
4 . The system in claim 2 , wherein:
the long-term predicting module is trained based on a long-term training dataset;
the short-term predicting module is trained based on a short-term training dataset;
the long-term training dataset is determined based on historical training data for the facility in a long-term time period; and
the short-term training dataset comprises historical residuals in a short-term time period from training the long-term predicting module.
5 . The system in claim 4 , wherein the computing instructions, when run on the one or more processors, further cause the one or more processors to perform:
detecting outlier historical data of the historical training data based on one or more of:
one or more equipment-type-based rules;
one or more statistics-based rules; or
an outlier-detection module trained based on one or more of: historical weather data, historical sensor data, or historical energy load profiles for the facility; and
removing the outlier historical data from the historical training data.
6 . The system in claim 1 , wherein determining, via the machine learning model, the predicted energy load profile further comprises determining, via the machine learning model, the predicted energy load profile based on one or more of:
a size of the facility;
one or more geographic features for the facility;
a footfall of the facility;
one or more assets for the facility; or
operating hours for the facility.
7 . The system in claim 1 , wherein determining the one or more demand shedding time slots further comprises:
determining a cut-off threshold based on historical peak energy consumption data of historical energy load profiles; and
determining the one or more demand shedding time slots based on the predicted energy load profile, the cut-off threshold, and the peak periods.
8 . The system in claim 7 , wherein determining the one or more demand shedding time slots further comprises:
determining whether a demand-shedding-deferring event exists in the weather forecast data; and
upon determining that the demand-shedding-deferring event exists, excluding at least one deferrable time slot in a deferring time period from the one or more demand shedding time slots.
9 . The system in claim 8 , wherein:
determining whether the demand-shedding-deferring event exists further comprises comparing a first portion and a second portion of the weather forecast data;
the first portion is associated with a time period of the predicted energy load profile; and
the second portion is associated with a subsequent time period immediately following the time period.
10 . The system in claim 1 , wherein:
the one or more demand shedding events for the one or more devices to be scheduled during the one or more demand shedding time slots further comprises one or more of:
changing a respective setting for at least one adjustable device of the one or more devices during at least one of the one or more demand shedding time slots based at least in part on the predicted energy load profile; or
switching a respective energy source for at least one of the one or more devices during at least one of the one or more demand shedding time slots based on operating configurations of one or more alternative energy sources for the facility.
11 . A method being implemented via execution of computing instructions configured to run at one or more processors and stored at one or more non-transitory computer-readable media, the method comprising:
determining, via a machine learning model, a predicted energy load profile for a facility based at least in part on weather forecast data and sensor data for the facility, wherein:
the sensor data are received from one or more energy monitoring sensors for one or more devices in the facility;
determining one or more demand shedding time slots based at least in part on peak periods and the predicted energy load profile;
determining one or more demand shedding events for the one or more devices to be scheduled during the one or more demand shedding time slots; and
causing a respective performance of each of the one or more demand shedding events by the one or more devices during the one or more demand shedding time slots.
12 . The method in claim 11 , wherein:
the machine learning model comprises an ensemble of a long-term predicting module and a short-term predicting module.
13 . The method in claim 12 , wherein determining, via the machine learning model, the predicted energy load profile further comprises:
determining, via the long-term predicting module, a first portion of the predicted energy load profile based at least in part on the weather forecast data and the sensor data for the facility; and
after determining the first portion, determining, via the short-term predicting module, a remaining portion of the predicted energy load profile based on residuals from the long-term predicting module.
14 . The method in claim 12 , wherein:
the long-term predicting module is trained based on a long-term training dataset;
the short-term predicting module is trained based on a short-term training dataset;
the long-term training dataset is determined based on historical training data for the facility in a long-term time period; and
the short-term training dataset comprises historical residuals in a short-term time period from training the long-term predicting module.
15 . The method in claim 14 , further comprising:
detecting outlier historical data of the historical training data based on one or more of:
one or more equipment-type-based rules;
one or more statistics-based rules; or
an outlier-detection module trained based on one or more of: historical weather data, historical sensor data, or historical energy load profiles for the facility; and
removing the outlier historical data from the historical training data.
16 . The method in claim 11 , wherein determining, via the machine learning model, the predicted energy load profile further comprises determining, via the machine learning model, the predicted energy load profile based on one or more of:
a size of the facility;
one or more geographic features for the facility;
a footfall of the facility;
one or more assets for the facility; or
operating hours for the facility.
17 . The method in claim 11 , wherein determining the one or more demand shedding time slots further comprises:
determining a cut-off threshold based on historical peak energy consumption data of historical energy load profiles; and
determining the one or more demand shedding time slots based on the predicted energy load profile, the cut-off threshold, and the peak periods.
18 . The method in claim 17 , wherein determining the one or more demand shedding time slots further comprises:
determining whether a demand-shedding-deferring event exists in the weather forecast data; and
upon determining that the demand-shedding-deferring event exists, excluding at least one deferrable time slot in a deferring time period from the one or more demand shedding time slots.
19 . The method in claim 18 , wherein:
determining whether the demand-shedding-deferring event exists further comprises comparing a first portion and a second portion of the weather forecast data;
the first portion is associated with a time period of the predicted energy load profile; and
the second portion is associated with a subsequent time period immediately following the time period.
20 . The method in claim 11 , wherein:
the one or more demand shedding events for the one or more devices to be scheduled during the one or more demand shedding time slots further comprises one or more of:
changing a respective setting for at least one adjustable device of the one or more devices during at least one of the one or more demand shedding time slots based at least in part on the predicted energy load profile; or
switching a respective energy source for at least one of the one or more devices during at least one of the one or more demand shedding time slots based on operating configurations of one or more alternative energy sources for the facility.