Intelligent Load Forecasting for Central Air Conditioning Using an Optimized Hybrid Deep Learning Framework

Accurate load forecasting of central air conditioning (CAC) systems is crucial for enhancing energy efficiency and minimizing operational costs. However, the complex nonlinear correlations among meteorological factors, water system dynamics, and cooling demand make this task challenging. To address...

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Veröffentlicht in:	Energies (Basel) Jg. 18; H. 21; S. 5736
Hauptverfasser:	He, Wei, Hua, Rui, Xiao, Yulong, Liu, Yuce, Zhou, Chaohui, Li, Chaoshun
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Basel MDPI AG 01.11.2025
Schlagworte:	Accuracy Air conditioning Algorithms Analysis BiGRU BiTCN Calibration Carbon central air conditioning Climate change Cooling Deep learning Energy conservation Energy consumption Energy efficiency Forecasting load forecasting Machine learning Mathematical optimization Neural networks Office buildings Optimization algorithms Regression analysis self-attention mechanism Statistical methods Support vector machines Temperature China
ISSN:	1996-1073, 1996-1073
Online-Zugang:	Volltext
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