US-20260124953-A1 - BUILDING LOAD MANAGEMENT DEVICE AND METHOD

US20260124953A1US 20260124953 A1US20260124953 A1US 20260124953A1US-20260124953-A1

Abstract

A building load management device includes a processor and a memory configured to store one or more programs executed by the processor. The processor includes a first processing unit configured to perform clustering, a second processing unit configured to predict a load of a first building at a future time using the previous building load data, a third processing unit configured to predict a load of a first cluster at the future time, and a fourth processing unit configured to assign a weight to a load prediction value of the first building and a load prediction value of the first cluster using a distance value between a centroid of the first cluster and the first building and calculate a final load prediction value of the first building using the weighted load prediction value of the first building and the weighted load prediction value of the first cluster.

Inventors

Min Kyu Lee
Hyun Sup Kim
Jae Yun JUNG
Jeong Hoon Choi
Na Yeon Park
Dae Gun Ko
Hye Seung Han
Sung Kyu Kim
Bum Su Park

Assignees

HYUNDAI MOTOR COMPANY
KIA CORPORATION

Dates

Publication Date: 20260507
Application Date: 20250512
Priority Date: 20241101

Claims (20)

1 . A building load management device comprising: a memory storing computer-executable instructions; and at least one processor configured to access the memory and execute the instructions, wherein the processor comprises: a first processing unit configured to perform clustering based on previous building load data stored in the memory to cluster a plurality of buildings, and to calculate distance values between a centroid of a cluster and buildings included in the cluster; a second processing unit configured to predict a load of a first building at a future time using the previous building load data; a third processing unit configured to predict a load of a first cluster at the future time using previous building load data of the first cluster including the first building; a fourth processing unit configured to assign a weight to a load prediction value of the first building, and a load prediction value of the first cluster using a distance value between a centroid of the first cluster and the first building and calculate a final load prediction value of the first building using the weighted load prediction value of the first building and the weighted load prediction value of the first cluster; and a fifth processing unit configured to generate a charging and discharging schedule of an electric vehicle using the final load prediction value of the first building.
2 . The building load management device of claim 1 , wherein the first processing unit is configured to: calculate a load pattern for a predetermined previous period from the previous building load data calculate at least one of a Euclidean distance value from a load average, a feature vector, and a dynamic time warping (DTW) distance value using the load pattern; and cluster the plurality of buildings using at least one of the Euclidean distance value, the feature vector, and the DTW distance value.
3 . The building load management device of claim 2 , wherein the first processing unit is configured to cluster the plurality of buildings by applying at least one of the Euclidean distance value, the feature vector, and the DTW distance value to a K-means algorithm.
4 . The building load management device of claim 1 , wherein the second processing unit includes a first deep learning model trained using first training data including building load data, weather data, and day of a week data for a predetermined previous period of the first building.
5 . The building load management device of claim 1 , wherein the third processing unit includes a second deep learning model trained using second training data including building load data, weather data, and day of a week data for a predetermined previous period of each building included in the first cluster.
6 . The building load management device of claim 5 , wherein the third processing unit is configured to train the second deep learning model by normalizing the building load data of the second training data.
7 . The building load management device of claim 1 , wherein the fourth processing unit is configured to increase a weight of the load prediction value of the first building in proportion to the distance value between the centroid of the first cluster and the first building, and to decrease the load prediction value of the first cluster.
8 . The building load management device of claim 1 , wherein the fourth processing unit is configured to calculate the final load prediction value of the first building by adding the weighted load prediction value of the first building and the weighted load prediction value of the first cluster.
9 . The building load management device of claim 1 , wherein the processor is configured to calculate a final weekday load prediction value of the first building using weekday building load, and a final weekend load prediction value of the first building using weekend building load data.
10 . The building load management device of claim 1 , wherein the processor is configured to transmit the charging and discharging schedule to an external server.
11 . A building load management method, which is performed by a computing device including a memory storing computer-executable instructions; and at least one processor configured to access the memory and execute the instructions, wherein the instructions comprise: performing, by the processor, clustering based on previous building load data stored in the memory and clustering a plurality of buildings; calculating, by the processor, distance values between a centroid of a cluster and buildings included in the cluster; predicting, by the processor, a load of a first building at a future time using the previous building load data; predicting, by the processor, a load of a first cluster at the future time point using previous building load data of the first cluster including the first building; assigning, by the processor, a weight to a load prediction value of the first building and a load prediction value of the first cluster using a distance value between a centroid of the first cluster and the first building; calculating, by the processor, a final load prediction value of the first building using the weighted load prediction value of the first building and the weighted load prediction value of the first cluster; and generating a charging and discharging schedule of an electric vehicle using the final load prediction value of the first building.
12 . The method of claim 11 , wherein clustering of the plurality of buildings includes: calculating a load pattern for a predetermined previous period from the previous building load data; calculating at least one of a Euclidean distance value from a load average, a feature vector, and a dynamic time warping (DTW) distance value using the load pattern; and clustering the plurality of buildings using at least one of the Euclidean distance value, the feature vector, and the DTW distance value.
13 . The method of claim 12 , wherein clustering of the plurality of buildings includes applying at least one of the Euclidean distance value, the feature vector, and the DTW distance value to a K-means algorithm to cluster the plurality of buildings.
14 . The method of claim 11 , wherein predicting of the load of the first building includes predicting a load of the first building using a first deep learning model trained using first training data including building load data, weather data, and day of a week data for a predetermined previous period of the first building.
15 . The method of claim 11 , wherein predicting of the load of the first cluster includes predicting a load of the first cluster using a second deep learning model trained using second training data including building load data, weather data, and day of a week data for a predetermined previous period of each building included in the first cluster.
16 . The method of claim 15 , wherein predicting of the load of the first cluster includes normalizing the building load data of the second training data to train the second deep learning model.
17 . The method of claim 11 , wherein assigning of the weight includes increasing a weight of the load prediction value of the first building in proportion to the distance value between the centroid of the first cluster and the first building, and decreasing the load prediction value of the first cluster.
18 . The method of claim 11 , wherein calculating of the final load prediction value of the first building includes summing the weighted load prediction value of the first building and the weighted load prediction value of the first cluster to calculate the final load prediction value of the first building.
19 . The method of claim 11 , wherein processor calculates a final weekday load prediction value of the first building using weekday building load data, and a final weekend load prediction value of the first building using weekend building load data.
20 . A building load management method, which is performed by a computing device including a memory storing computer-executable instructions; and at least one processor configured to access the memory and execute the instructions, wherein the instructions comprise: performing, by the processor, clustering based on previous building load data stored in the memory and clustering a plurality of buildings; calculating, by the processor, distance values between a centroid of a cluster and buildings included in the cluster; predicting, by the processor, a load of a first building at a future time using the previous building load data; predicting, by the processor, a load of a first cluster at the future time point using previous building load data of the first cluster including the first building; assigning, by the processor, a weight to a load prediction value of the first building and a load prediction value of the first cluster using a distance value between a centroid of the first cluster and the first building; calculating, by the processor, a final load prediction value of the first building using the weighted load prediction value of the first building and the weighted load prediction value of the first cluster; transmitting a predicted final building load value to an electric vehicle charging platform; and performing charging and discharging scheduling, and charging and discharging control on the electric vehicle charging platform.

Description

CROSS REFERENCE TO RELATED APPLICATIONS This application claims priority to and the benefit of Korean Patent Application No. 10-2024-0153325, filed on Nov. 1, 2024, the disclosure of which is incorporated herein by reference in its entirety. BACKGROUND 1. Field One embodiment of the present disclosure relates to a building load management device and method, and more specifically, to a building load management device and method, which are capable of being applied to a vehicle-to-building (V2B) platform. 2. Discussion of Related Art With the advancement of technologies of electric vehicles, a vehicle-to-grid (V2G) technology in which the electric vehicle is regarded as an individual energy storage system (ESS) and connected to a power grid is being developed, and such a V2G technology can be seen as solving a stabilization problem of the demand and supply of the power grid using a battery of the electric vehicle. Depending on where electricity is used, such a technology is called a vehicle-to-home (V2H), a vehicle-to-building (V2B), a V2G, or the like. Among these, the V2B, which supplies power of the battery of the electric vehicle to buildings, is being demonstrated and attempts are being made to commercialize the V2G in the future. When the required power exceeds a maximum load of a building, contracted power may be increased, which may lead to an increase in a basic electricity rate, and securing available resources for electric vehicles through accurate load prediction is emerging as an important issue as a method for commercializing the V2B. SUMMARY The present disclosure is directed to a building load management device and method, which are capable of predicting an accurate building load. In addition, the present disclosure is directed to a building load management device and method, which are capable of increasing efficiency of a vehicle-to-building (V2B) by performing charging and discharging control of an electric vehicle through building load prediction. According to an aspect of the present disclosure, there is provided a building load management device including one or more processors, and a memory configured to store one or more programs executed by the one or more processors, wherein the processor includes a first processing unit configured to perform clustering based on previous building load data stored in the memory to cluster a plurality of buildings and calculate distance values between a centroid of a cluster and buildings included in the cluster, a second processing unit configured to predict a load of a first building at a future time point using the previous building load data, a third processing unit configured to predict a load of a first cluster at the future time point using previous building load data of the first cluster including the first building, and a fourth processing unit configured to assign a weight to a load prediction value of the first building and a load prediction value of the first cluster using a distance value between a centroid of the first cluster and the first building and calculate a final load prediction value of the first building using the weighted load prediction value of the first building and the weighted load prediction value of the first cluster. The first processing unit may calculate a load pattern for a predetermined previous period from the previous building load data, calculate at least one of a Euclidean distance value from a load average, a feature vector, and a dynamic time warping (DTW) distance value using the load pattern, and cluster the plurality of buildings using at least one of the Euclidean distance value, the feature vector, and the DTW distance value. The first processing unit may cluster the plurality of buildings by applying at least one of the Euclidean distance value, the feature vector, and the DTW distance value to a K-means algorithm. The second processing unit may include a first deep learning model trained using first training data including building load data, weather data, and day of the week data for a predetermined previous period of the first building. The third processing unit may include a second deep learning model trained using second training data including building load data, weather data, and day of the week data for a predetermined previous period of each building included in the first cluster. The third processing unit may train the second deep learning model by normalizing the building load data of the second training data. The fourth processing unit may increase a weight of the load prediction value of the first building in proportion to the distance value between the centroid of the first cluster and the first building and decrease the load prediction value of the first cluster. The fourth processing unit may calculate the final load prediction value of the first building by summing the weighted load prediction value of the first building and the weighted load prediction value of the first cluster. The proc