Air Conditioning Load Forecasting for Geographical Grids Using Deep Reinforcement Learning and Density-Based Spatial Clustering of Applications with Noise and Graph Attention Networks

Air Conditioning Load Forecasting for Geographical Grids Using Deep Reinforcement Learning and Density-Based Spatial Clustering of Applications with Noise and Graph Attention Networks

Air conditioning loads in power systems exhibit spatiotemporal heterogeneity across geographical regions, complicating accurate load forecasting. This study proposes a framework that integrates Deep Reinforcement Learning-guided DBSCAN (DRL-DBSCAN) clustering with a Graph Attention Network (GAT)-bas...

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Bibliographic Details
Main Authors: Chuan Long, Xinting Yang, Yunche Su, Fang Liu, Ruiguang Ma, Tiannan Ma, Yangjin Wu, Xiaodong Shen
Format: Article
Language:English
Published: MDPI AG 2025-05-01
Series:Energies
Subjects:
air conditioning load forecasting
graph attention network (GAT)
geographical grids
DRL-DBSCAN
spatial dependencies
Online Access:https://www.mdpi.com/1996-1073/18/11/2832
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