Air Conditioning Load Data Generation Method Based on DTW Clustering and Physically Constrained TimeGAN.

Area of Science:

• Energy Systems Engineering
• Artificial Intelligence
• Data Science

Background:

• Air-conditioning load data generation is vital for energy management but faces challenges due to data non-stationarity, complex influencing factors, and limitations of existing models.
• Current models often produce averaged distributions, losing specific temporal patterns, and lack physical interpretability due to the absence of explicit constraints.

Purpose of the Study:

• To develop a hybrid generation framework that accurately models diverse temporal patterns and ensures physical interpretability for air-conditioning load data.
• To overcome the limitations of existing data-driven models by integrating clustering, physically-constrained generative adversarial networks, and adaptive model selection.

Main Methods:

• A hybrid framework combining DTW clustering for data partitioning, a physically-constrained TimeGAN (Generative Adversarial Network) with intrinsic temperature-load correlations for thermodynamic consistency, and an LSTM (Long Short-Term Memory)-based mechanism for adaptive submodel selection.
• DTW clustering segments data to model diverse temporal patterns; parameter-free physical constraints ensure thermodynamic consistency in sensor-scarce environments; LSTM dynamically selects submodels for adaptive temporal fusion.

Main Results:

• The proposed framework achieved a local similarity score of 0.98 on air-conditioning load datasets from Southeast China.
• Outperformed state-of-the-art models by 11.4% and the original TimeGAN by 13.3% in generating accurate and physically consistent load data.

Conclusions:

• The hybrid framework effectively addresses the challenges of non-stationarity and lack of physical interpretability in air-conditioning load data generation.
• This approach enhances the accuracy and reliability of generated load data, supporting improved power grid scheduling and intelligent energy management.