Ms. Qian Liu is an emerging researcher in building energy systems and data-driven energy analytics, with a focused scholarly contribution to electricity consumption forecasting and energy efficiency in the built environment. Her research emphasizes advanced machine learning methodologies, particularly multi-model ensemble learning and cross-domain robust stacking techniques, to improve the accuracy and reliability of heating-season electricity demand prediction. She has authored 3 research documents, which have collectively received 1 citation from 1 citing document, and she currently holds an h-index of 1, reflecting early but measurable academic impact. Her recent work published in Energy and Buildings (2026) demonstrates the integration of heterogeneous models to enhance predictive performance across varying climatic and operational conditions, offering practical insights for smart energy management and sustainable building operations. Overall, her research contributes to the growing intersection of energy engineering, artificial intelligence, and building performance optimization, supporting data-informed decision-making for future low-energy and resilient building systems.