- Developing a novel personal thermoelectric comfort system for improving indoor occupant’s thermal comfort. Dans: Journal of Building Engineering, v. 84 (mai 2024). (2024):
- Data efficient indoor thermal comfort prediction using instance based transfer learning method. Dans: Energy and Buildings, v. 306 (mars 2024). (2024):
- An instance based multi-source transfer learning strategy for building’s short-term electricity loads prediction under sparse data scenarios. Dans: Journal of Building Engineering, v. 85 (mai 2024). (2024):
- Non-invasive human thermal comfort assessment based on multiple angle/distance facial key-region temperatures recognition. Dans: Building and Environment, v. 246 (décembre 2023). (2023):
- Fast reconstruction of indoor temperature field for large-space building based on limited sensors: An experimental study. Dans: Energy and Buildings, v. 298 (novembre 2023). (2023):
- Correlation analysis and modeling of human thermal sensation with multiple physiological markers: An experimental study. Dans: Energy and Buildings, v. 278 (janvier 2023). (2023):
- Building’s hourly electrical load prediction based on data clustering and ensemble learning strategy. Dans: Energy and Buildings, v. 261 (avril 2022). (2022):
- An adaptive ensemble predictive strategy for multiple scale electrical energy usages forecasting. Dans: Sustainable Cities and Society, v. 66 (mars 2021). (2021):
- A state of the art review on the prediction of building energy consumption using data-driven technique and evolutionary algorithms. Dans: Building Services Engineering Research and Technology, v. 41, n. 1 (décembre 2019). (2019):
- Short-term electricity consumption prediction for buildings using data-driven swarm intelligence based ensemble model. Dans: Energy and Buildings, v. 231 (janvier 2021). (2021):
- Optimization of ventilation system operation in office environment using POD model reduction and genetic algorithm. Dans: Energy and Buildings, v. 67 (décembre 2013). (2013):
- Building's electricity consumption prediction using optimized artificial neural networks and principal component analysis. Dans: Energy and Buildings, v. 108 (décembre 2015). (2015):
- A hybrid teaching-learning artificial neural network for building electrical energy consumption prediction. Dans: Energy and Buildings, v. 174 (septembre 2018). (2018):