Jiangyan Liu

TL;DR: This study presents a prediction strategy of building energy consumption based on ensemble learning and energy consumption patterns classification and illustrates that the proposed strategy is reliable and effective and can obtain acceptable performance with less training data, which is helpful to the application of energy consumption prediction.

TL;DR: A transfer-learning-based methodology for fault diagnosis in building chillers and the experimental results validate the value of transfer learning for FDD in building energy systems, especially when the experimental data available for model development are limited.

TL;DR: A method that can conduct both fault diagnosis and fault action mechanism explanation of building chillers, and the CBA-based fault diagnosis model can well identify seven common chiller faults with an overall diagnostic accuracy of 90.15%.

TL;DR: The Intelligent Model Predictive Control strategy, integrating the vehicle speed previewer and the self-adaptor of passenger’s thermal comfort, is proposed and applied to the AC-cabin system and saves more energy than the other control strategies researched in this paper.

TL;DR: The proposed intelligent air conditioning system control strategy that can learn passengers’ thermal comfort preferences can improve the thermal comfort of passengers, compared with the on-off and fuzzy PID controllers; moreover, it consumes less energy.