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

Chuan Long, Xinting Yang, Yunche Su, Fang Liu, Ruiguang Ma, Tiannan Ma, Yangjin Wu and Xiaodong Shen ()
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Chuan Long: State Grid Sichuan Economic Research Institute, Chengdu 610041, China
Xinting Yang: State Grid Sichuan Economic Research Institute, Chengdu 610041, China
Yunche Su: State Grid Sichuan Economic Research Institute, Chengdu 610041, China
Fang Liu: State Grid Sichuan Economic Research Institute, Chengdu 610041, China
Ruiguang Ma: State Grid Sichuan Economic Research Institute, Chengdu 610041, China
Tiannan Ma: State Grid Sichuan Economic Research Institute, Chengdu 610041, China
Yangjin Wu: College of Electrical Engineering, Sichuan University, Chengdu 610065, China
Xiaodong Shen: College of Electrical Engineering, Sichuan University, Chengdu 610065, China

Energies, 2025, vol. 18, issue 11, 1-19

Abstract: 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)-based Graph Neural Network to model spatial dependencies and temporal dynamics. Using meteorological features like temperature and humidity, the framework clusters geographical grids and applies GAT to capture spatial patterns. On a Pecan Street dataset of 25 households in Austin, the GAT with DRL-DBSCAN achieves a Test MSE of 0.0216 and MAE of 0.0884, outperforming K-Means (MSE: 0.0523, MAE: 0.1456), Hierarchical clustering (MSE: 0.0478, MAE: 0.1321), no-clustering (MSE: 0.0631, MAE: 0.1678), LSTM (MSE: 0.3259, MAE: 0.3442), Transformer (MSE: 0.6415, MAE: 0.4835), and MLP (MSE: 0.7269, MAE: 0.5240) baselines. This approach enhances forecasting accuracy for real-time grid management and energy efficiency in smart grids, though further refinement is needed for standardizing predicted load ranges.

Keywords: air conditioning load forecasting; graph attention network (GAT); geographical grids; DRL-DBSCAN; spatial dependencies (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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