.png){kind=icon}
返回
Graph convolutional network-based aggregated demand response baseline load estimation
.png){kind=icon}
PRE
AI
DOI：10.1016/j.energy.2022.123847.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Accurate aggregated baseline load (ABL) estimation is very important for the demand response (DR) compensation settlement between system operators and DR aggregators. Current ABL estimation methods totally ignore the spatial correlation between load patterns of different customers, which will lead to large errors when the load pattern on the DR event day fluctuates largely compared with that in historical days. To this end, this paper proposes a Graph Convolutional Network (GCN)-based ABL estimation method to improve the estimation accuracy. The basic idea is to enhance the estimator's ability to capture load uncertainty by sharing load fluctuation information between different customers. The proposed method contains three main steps: First, all customers are grouped into different clusters by the K-means algorithm according to their historical typical load patterns (TLPs). Second, these clusters are transformed into an undirected graph based on an adjacency matrix reflecting the spatial correlations, which are constructed according to the difference between the TLPs in different clusters. Third, the ABL estimation is transformed into a node regression problem of the graph. Case studies on a real load dataset verify the effectiveness and superiority of the proposed method.(c) 2022 Elsevier Ltd. All rights reserved.
Keyword:
Demand response
Aggregated baseline load
Graph convolutional network
Adjacency matrix
Spatial correlation
期刊
IF:
9.4
论文数:
4.2W
被引数:
20.2W
机构
引用论文
A hybrid demand response mechanism based on real-time incentive and real-time pricing基于实时激励和实时定价的混合需求响应机制.png)
ENERGY
OAAI
ENERGYIF9.4
OAAI分享收藏Smart Households' Aggregated Capacity Forecasting for Load Aggregators Under Incentive-Based Demand Response Programs基于激励的需求响应计划下智能家庭对负载聚合器的聚合容量预测
PREAI
分享收藏Short-term residential electric load forecasting: A compressive spatio-temporal approachENERGY AND BUILDINGS
ENERGY AND BUILDINGSIF7.1
PREAI
分享收藏Day-ahead optimal bidding and scheduling strategies for DER aggregator considering responsive uncertainty under real-time pricingENERGY
ENERGYIF9.4
PREAI
分享收藏Energy Sharing Management for Microgrids With PV Prosumers: A Stackelberg Game Approach具有光伏产用户的微电网的能量共享管理: Stackelberg博弈方法
PREAI
分享收藏分享收藏
查看更多内容