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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Published in:Energies (Basel) Vol. 18; no. 21; p. 5736
Main Authors: He, Wei, Hua, Rui, Xiao, Yulong, Liu, Yuce, Zhou, Chaohui, Li, Chaoshun
Format: Journal Article
Language:English
Published: Basel MDPI AG 01.11.2025
Subjects:
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 Access:Get full text
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Summary: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 these issues, this study proposes a novel hybrid forecasting model termed IWOA-BiTCN-BiGRU-SA, which integrates the Improved Whale Optimization Algorithm (IWOA), Bidirectional Temporal Convolutional Networks (BiTCN), Bidirectional Gated Recurrent Units (BiGRU), and a Self-attention mechanism (SA). BiTCN is adopted to extract temporal dependencies and multi-scale features, BiGRU captures long-term bidirectional correlations, and the self-attention mechanism enhances feature weighting adaptively. Furthermore, IWOA is employed to optimize the hyperparameters of BiTCN and BiGRU, improving training stability and generalization. Experimental results based on real CAC operational data demonstrate that the proposed model outperforms traditional methods such as LSTM, GRU, and TCN, as well as hybrid deep learning benchmark models. Compared to all comparison models, the root mean square error (RMSE) decreased by 13.72% to 56.66%. This research highlights the application potential of the IWSO-BiTCN-BiGRU-Attention framework in practical load forecasting and intelligent energy management for large-scale CAC systems.
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ISSN:1996-1073
1996-1073
DOI:10.3390/en18215736