IGANs With Event-Driven Pseudo Features for Dynamic Response Modeling in Building Air Conditioning Systems

To address the unavailability of indoor temperature data and limited model generalization in dynamic response modeling of building air conditioning systems (ACs), a framework combining event-driven pseudo-feature generation and an improved generative adversarial networks (IGANs) architecture is proposed. By utilizing data from a data-sufficient source domain, an event-driven algorithm is applied to generate pseudo indoor features and improve the inference accuracy of indoor information. The dynamic response modeling of the target user's AC system is then reformulated into a data-driven, multi-step time series prediction problem, where a reconstructed dataset consisting of the generated pseudo features, serves as the model input. Then, an IGANs architecture, specifically designed for regression tasks, is introduced, along with the corresponding training strategy that balances spatial alignment and discrepancy capture, improving model performance. Comparative experiments on real-world datasets validate the effectiveness of the proposed method, and sensitivity analysis further reveals the impact of potential factors on the results.