Yakai Lu 
- (2024): Limited data-oriented building heating load prediction method: A novel meta learning-based framework. In: Energy and Buildings, v. 308 (April 2024).
- (2024): A three-dimensional evaluation method for building energy systems to guide power grid-friendly interactions during the planning and operational stages. In: Journal of Building Engineering, v. 86 (Juni 2024).
- (2023): Load extraction from actual operation data for data-driven ultra-short-term room air-conditioning load prediction. In: Energy and Buildings, v. 296 (Oktober 2023).
- (2023): Experimental study on performance assessments of HVAC cross-domain fault diagnosis methods oriented to incomplete data problems. In: Building and Environment, v. 236 (Mai 2023).
- (2023): Energy-saving potential analysis for a 24-hour operating chiller plant using the model-based global optimization method. In: Journal of Building Engineering, v. 69 (Juni 2023).
- (2023): Imbalanced data-oriented model learning method for ultra-short-term air conditioning load prediction. In: Energy and Buildings, v. 286 (Mai 2023).
- (2022): A study on transfer learning in enhancing performance of building energy system fault diagnosis with extremely limited labeled data. In: Building and Environment, v. 225 (November 2022).
- (2022): Research on design day generation method for air-conditioning system design considering the coincidence of hourly variation coefficient. In: Energy and Buildings, v. 270 (September 2022).
- (2022): An energy performance assessment method for district heating substations based on energy disaggregation. In: Energy and Buildings, v. 255 (Januar 2022).
- (2021): Non-intrusive measurement of building cooling load components via morphological components analysis. In: Journal of Building Engineering, v. 41 (September 2021).
- (2021): Multi-step-ahead prediction of thermal load in regional energy system using deep learning method. In: Energy and Buildings, v. 233 (Februar 2021).
- (2019): Identification and evaluation of operation regulation strategies in district heating substations based on an unsupervised data mining method. In: Energy and Buildings, v. 202 (November 2019).
- (2019): GMM clustering for heating load patterns in-depth identification and prediction model accuracy improvement of district heating system. In: Energy and Buildings, v. 190 (Mai 2019).
- (2018): Stepwise calibration for residential building thermal performance model using hourly heat consumption data. In: Energy and Buildings, v. 181 (Dezember 2018).
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