Xilei Dai | Engineering | Young Scientist Award Published on 22/09/202522/09/2025 by Environmental Scientists Prof. Dr. Xilei Dai | Engineering | Young Scientist Award Professor | Chongqing University | China Prof. Dr. Xilei Dai is a distinguished faculty member at the School of Architecture and Urban Planning, Chongqing University, with recognized expertise in the fields of smart building control, energy optimization, and sustainable building technologies. His academic profile is well established with 2,206 citations by 2,085 documents, 38 documents published, and an h-index of 17, demonstrating the strong impact and relevance of his research. He has influential publications in top-tier journals such as Applied Energy, Building Simulation, Building and Environment, and Energy and Buildings. One of his highly influential papers on green building has been widely acknowledged by peers, earning significant citations and recognition as an ESI highly cited work. His editorial contributions include serving as an associate editor for the Journal of Renewable and Sustainable Energy, further underlining his commitment to advancing knowledge in renewable energy and sustainability. Prof. Dr. Xilei Dai research focuses on building decarbonization strategies by integrating IoT-based technologies, artificial intelligence control systems, distributed energy resources, and battery storage, offering impactful solutions that reduce both energy consumption and carbon emissions. He has successfully led innovative projects such as the development of NetZero Building Energy Management Systems through AI-driven HVAC system control, reflecting his dedication to creating practical solutions with far-reaching implications for sustainable urban development. His research not only addresses pressing environmental challenges but also bridges the gap between optimization theory and real-world applications in energy system economics. With an expanding body of highly cited work, editorial leadership, and a strong focus on innovative building energy systems, Prof. Dr. Xilei Dai continues to contribute significantly to the global effort of achieving smart, efficient, and environmentally responsible building environments. Profile: Scopus | Orcid | Google Scholar | Staff Page | Featured Publications Cao, X., Dai, X., & Liu, J. (2016). Building energy-consumption status worldwide and the state-of-the-art technologies for zero-energy buildings during the past decade. Energy and Buildings, 128, 198–213. Dai, X., Liu, J., & Zhang, X. (2020). A review of studies applying machine learning models to predict occupancy and window-opening behaviours in smart buildings. Energy and Buildings, 223, 110159. Liu, J., Dai, X., Li, X., Jia, S., Pei, J., Sun, Y., Lai, D., Shen, X., Sun, H., Yin, H., … (2018). Indoor air quality and occupants’ ventilation habits in China: Seasonal measurement and long-term monitoring. Building and Environment, 142, 119–129. Dai, X., Huang, L., Qian, Y., Xia, S., Chong, W., Liu, J., Ieva, A. D., Hou, X., & Ou, C. (2020). Deep learning for automated cerebral aneurysm detection on computed tomography images. International Journal of Computer Assisted Radiology and Surgery, 15, 715–723. Dai, X., Liu, J., Li, X., & Zhao, L. (2018). Long-term monitoring of indoor CO2 and PM2.5 in Chinese homes: Concentrations and their relationships with outdoor environments. Building and Environment, 144, 238–247.
Prof. Dr. Xilei Dai is a distinguished faculty member at the School of Architecture and Urban Planning, Chongqing University, with recognized expertise in the fields of smart building control, energy optimization, and sustainable building technologies. His academic profile is well established with 2,206 citations by 2,085 documents, 38 documents published, and an h-index of 17, demonstrating the strong impact and relevance of his research. He has influential publications in top-tier journals such as Applied Energy, Building Simulation, Building and Environment, and Energy and Buildings. One of his highly influential papers on green building has been widely acknowledged by peers, earning significant citations and recognition as an ESI highly cited work. His editorial contributions include serving as an associate editor for the Journal of Renewable and Sustainable Energy, further underlining his commitment to advancing knowledge in renewable energy and sustainability. Prof. Dr. Xilei Dai research focuses on building decarbonization strategies by integrating IoT-based technologies, artificial intelligence control systems, distributed energy resources, and battery storage, offering impactful solutions that reduce both energy consumption and carbon emissions. He has successfully led innovative projects such as the development of NetZero Building Energy Management Systems through AI-driven HVAC system control, reflecting his dedication to creating practical solutions with far-reaching implications for sustainable urban development. His research not only addresses pressing environmental challenges but also bridges the gap between optimization theory and real-world applications in energy system economics. With an expanding body of highly cited work, editorial leadership, and a strong focus on innovative building energy systems, Prof. Dr. Xilei Dai continues to contribute significantly to the global effort of achieving smart, efficient, and environmentally responsible building environments. Profile: Scopus | Orcid | Google Scholar | Staff Page |
Prof. Dr. Xilei Dai is a distinguished faculty member at the School of Architecture and Urban Planning, Chongqing University, with recognized expertise in the fields of smart building control, energy optimization, and sustainable building technologies. His academic profile is well established with 2,206 citations by 2,085 documents, 38 documents published, and an h-index of 17, demonstrating the strong impact and relevance of his research. He has influential publications in top-tier journals such as Applied Energy, Building Simulation, Building and Environment, and Energy and Buildings. One of his highly influential papers on green building has been widely acknowledged by peers, earning significant citations and recognition as an ESI highly cited work. His editorial contributions include serving as an associate editor for the Journal of Renewable and Sustainable Energy, further underlining his commitment to advancing knowledge in renewable energy and sustainability. Prof. Dr. Xilei Dai research focuses on building decarbonization strategies by integrating IoT-based technologies, artificial intelligence control systems, distributed energy resources, and battery storage, offering impactful solutions that reduce both energy consumption and carbon emissions. He has successfully led innovative projects such as the development of NetZero Building Energy Management Systems through AI-driven HVAC system control, reflecting his dedication to creating practical solutions with far-reaching implications for sustainable urban development. His research not only addresses pressing environmental challenges but also bridges the gap between optimization theory and real-world applications in energy system economics. With an expanding body of highly cited work, editorial leadership, and a strong focus on innovative building energy systems, Prof. Dr. Xilei Dai continues to contribute significantly to the global effort of achieving smart, efficient, and environmentally responsible building environments. Profile: Scopus | Orcid | Google Scholar | Staff Page |
Prof. Dr. Xilei Dai is a distinguished faculty member at the School of Architecture and Urban Planning, Chongqing University, with recognized expertise in the fields of smart building control, energy optimization, and sustainable building technologies. His academic profile is well established with 2,206 citations by 2,085 documents, 38 documents published, and an h-index of 17, demonstrating the strong impact and relevance of his research. He has influential publications in top-tier journals such as Applied Energy, Building Simulation, Building and Environment, and Energy and Buildings. One of his highly influential papers on green building has been widely acknowledged by peers, earning significant citations and recognition as an ESI highly cited work. His editorial contributions include serving as an associate editor for the Journal of Renewable and Sustainable Energy, further underlining his commitment to advancing knowledge in renewable energy and sustainability. Prof. Dr. Xilei Dai research focuses on building decarbonization strategies by integrating IoT-based technologies, artificial intelligence control systems, distributed energy resources, and battery storage, offering impactful solutions that reduce both energy consumption and carbon emissions. He has successfully led innovative projects such as the development of NetZero Building Energy Management Systems through AI-driven HVAC system control, reflecting his dedication to creating practical solutions with far-reaching implications for sustainable urban development. His research not only addresses pressing environmental challenges but also bridges the gap between optimization theory and real-world applications in energy system economics. With an expanding body of highly cited work, editorial leadership, and a strong focus on innovative building energy systems, Prof. Dr. Xilei Dai continues to contribute significantly to the global effort of achieving smart, efficient, and environmentally responsible building environments. Profile: Scopus | Orcid | Google Scholar | Staff Page |
Prof. Dr. Xilei Dai is a distinguished faculty member at the School of Architecture and Urban Planning, Chongqing University, with recognized expertise in the fields of smart building control, energy optimization, and sustainable building technologies. His academic profile is well established with 2,206 citations by 2,085 documents, 38 documents published, and an h-index of 17, demonstrating the strong impact and relevance of his research. He has influential publications in top-tier journals such as Applied Energy, Building Simulation, Building and Environment, and Energy and Buildings. One of his highly influential papers on green building has been widely acknowledged by peers, earning significant citations and recognition as an ESI highly cited work. His editorial contributions include serving as an associate editor for the Journal of Renewable and Sustainable Energy, further underlining his commitment to advancing knowledge in renewable energy and sustainability. Prof. Dr. Xilei Dai research focuses on building decarbonization strategies by integrating IoT-based technologies, artificial intelligence control systems, distributed energy resources, and battery storage, offering impactful solutions that reduce both energy consumption and carbon emissions. He has successfully led innovative projects such as the development of NetZero Building Energy Management Systems through AI-driven HVAC system control, reflecting his dedication to creating practical solutions with far-reaching implications for sustainable urban development. His research not only addresses pressing environmental challenges but also bridges the gap between optimization theory and real-world applications in energy system economics. With an expanding body of highly cited work, editorial leadership, and a strong focus on innovative building energy systems, Prof. Dr. Xilei Dai continues to contribute significantly to the global effort of achieving smart, efficient, and environmentally responsible building environments. Profile: Scopus | Orcid | Google Scholar | Staff Page |
Prof. Dr. Xilei Dai is a distinguished faculty member at the School of Architecture and Urban Planning, Chongqing University, with recognized expertise in the fields of smart building control, energy optimization, and sustainable building technologies. His academic profile is well established with 2,206 citations by 2,085 documents, 38 documents published, and an h-index of 17, demonstrating the strong impact and relevance of his research. He has influential publications in top-tier journals such as Applied Energy, Building Simulation, Building and Environment, and Energy and Buildings. One of his highly influential papers on green building has been widely acknowledged by peers, earning significant citations and recognition as an ESI highly cited work. His editorial contributions include serving as an associate editor for the Journal of Renewable and Sustainable Energy, further underlining his commitment to advancing knowledge in renewable energy and sustainability. Prof. Dr. Xilei Dai research focuses on building decarbonization strategies by integrating IoT-based technologies, artificial intelligence control systems, distributed energy resources, and battery storage, offering impactful solutions that reduce both energy consumption and carbon emissions. He has successfully led innovative projects such as the development of NetZero Building Energy Management Systems through AI-driven HVAC system control, reflecting his dedication to creating practical solutions with far-reaching implications for sustainable urban development. His research not only addresses pressing environmental challenges but also bridges the gap between optimization theory and real-world applications in energy system economics. With an expanding body of highly cited work, editorial leadership, and a strong focus on innovative building energy systems, Prof. Dr. Xilei Dai continues to contribute significantly to the global effort of achieving smart, efficient, and environmentally responsible building environments. Profile: Scopus | Orcid | Google Scholar | Staff Page |
Featured Publications Cao, X., Dai, X., & Liu, J. (2016). Building energy-consumption status worldwide and the state-of-the-art technologies for zero-energy buildings during the past decade. Energy and Buildings, 128, 198–213. Dai, X., Liu, J., & Zhang, X. (2020). A review of studies applying machine learning models to predict occupancy and window-opening behaviours in smart buildings. Energy and Buildings, 223, 110159. Liu, J., Dai, X., Li, X., Jia, S., Pei, J., Sun, Y., Lai, D., Shen, X., Sun, H., Yin, H., … (2018). Indoor air quality and occupants’ ventilation habits in China: Seasonal measurement and long-term monitoring. Building and Environment, 142, 119–129. Dai, X., Huang, L., Qian, Y., Xia, S., Chong, W., Liu, J., Ieva, A. D., Hou, X., & Ou, C. (2020). Deep learning for automated cerebral aneurysm detection on computed tomography images. International Journal of Computer Assisted Radiology and Surgery, 15, 715–723. Dai, X., Liu, J., Li, X., & Zhao, L. (2018). Long-term monitoring of indoor CO2 and PM2.5 in Chinese homes: Concentrations and their relationships with outdoor environments. Building and Environment, 144, 238–247.
Featured Publications Cao, X., Dai, X., & Liu, J. (2016). Building energy-consumption status worldwide and the state-of-the-art technologies for zero-energy buildings during the past decade. Energy and Buildings, 128, 198–213. Dai, X., Liu, J., & Zhang, X. (2020). A review of studies applying machine learning models to predict occupancy and window-opening behaviours in smart buildings. Energy and Buildings, 223, 110159. Liu, J., Dai, X., Li, X., Jia, S., Pei, J., Sun, Y., Lai, D., Shen, X., Sun, H., Yin, H., … (2018). Indoor air quality and occupants’ ventilation habits in China: Seasonal measurement and long-term monitoring. Building and Environment, 142, 119–129. Dai, X., Huang, L., Qian, Y., Xia, S., Chong, W., Liu, J., Ieva, A. D., Hou, X., & Ou, C. (2020). Deep learning for automated cerebral aneurysm detection on computed tomography images. International Journal of Computer Assisted Radiology and Surgery, 15, 715–723. Dai, X., Liu, J., Li, X., & Zhao, L. (2018). Long-term monitoring of indoor CO2 and PM2.5 in Chinese homes: Concentrations and their relationships with outdoor environments. Building and Environment, 144, 238–247.
Featured Publications Cao, X., Dai, X., & Liu, J. (2016). Building energy-consumption status worldwide and the state-of-the-art technologies for zero-energy buildings during the past decade. Energy and Buildings, 128, 198–213. Dai, X., Liu, J., & Zhang, X. (2020). A review of studies applying machine learning models to predict occupancy and window-opening behaviours in smart buildings. Energy and Buildings, 223, 110159. Liu, J., Dai, X., Li, X., Jia, S., Pei, J., Sun, Y., Lai, D., Shen, X., Sun, H., Yin, H., … (2018). Indoor air quality and occupants’ ventilation habits in China: Seasonal measurement and long-term monitoring. Building and Environment, 142, 119–129. Dai, X., Huang, L., Qian, Y., Xia, S., Chong, W., Liu, J., Ieva, A. D., Hou, X., & Ou, C. (2020). Deep learning for automated cerebral aneurysm detection on computed tomography images. International Journal of Computer Assisted Radiology and Surgery, 15, 715–723. Dai, X., Liu, J., Li, X., & Zhao, L. (2018). Long-term monitoring of indoor CO2 and PM2.5 in Chinese homes: Concentrations and their relationships with outdoor environments. Building and Environment, 144, 238–247.
Featured Publications Cao, X., Dai, X., & Liu, J. (2016). Building energy-consumption status worldwide and the state-of-the-art technologies for zero-energy buildings during the past decade. Energy and Buildings, 128, 198–213. Dai, X., Liu, J., & Zhang, X. (2020). A review of studies applying machine learning models to predict occupancy and window-opening behaviours in smart buildings. Energy and Buildings, 223, 110159. Liu, J., Dai, X., Li, X., Jia, S., Pei, J., Sun, Y., Lai, D., Shen, X., Sun, H., Yin, H., … (2018). Indoor air quality and occupants’ ventilation habits in China: Seasonal measurement and long-term monitoring. Building and Environment, 142, 119–129. Dai, X., Huang, L., Qian, Y., Xia, S., Chong, W., Liu, J., Ieva, A. D., Hou, X., & Ou, C. (2020). Deep learning for automated cerebral aneurysm detection on computed tomography images. International Journal of Computer Assisted Radiology and Surgery, 15, 715–723. Dai, X., Liu, J., Li, X., & Zhao, L. (2018). Long-term monitoring of indoor CO2 and PM2.5 in Chinese homes: Concentrations and their relationships with outdoor environments. Building and Environment, 144, 238–247.
Featured Publications Cao, X., Dai, X., & Liu, J. (2016). Building energy-consumption status worldwide and the state-of-the-art technologies for zero-energy buildings during the past decade. Energy and Buildings, 128, 198–213. Dai, X., Liu, J., & Zhang, X. (2020). A review of studies applying machine learning models to predict occupancy and window-opening behaviours in smart buildings. Energy and Buildings, 223, 110159. Liu, J., Dai, X., Li, X., Jia, S., Pei, J., Sun, Y., Lai, D., Shen, X., Sun, H., Yin, H., … (2018). Indoor air quality and occupants’ ventilation habits in China: Seasonal measurement and long-term monitoring. Building and Environment, 142, 119–129. Dai, X., Huang, L., Qian, Y., Xia, S., Chong, W., Liu, J., Ieva, A. D., Hou, X., & Ou, C. (2020). Deep learning for automated cerebral aneurysm detection on computed tomography images. International Journal of Computer Assisted Radiology and Surgery, 15, 715–723. Dai, X., Liu, J., Li, X., & Zhao, L. (2018). Long-term monitoring of indoor CO2 and PM2.5 in Chinese homes: Concentrations and their relationships with outdoor environments. Building and Environment, 144, 238–247.
Featured Publications Cao, X., Dai, X., & Liu, J. (2016). Building energy-consumption status worldwide and the state-of-the-art technologies for zero-energy buildings during the past decade. Energy and Buildings, 128, 198–213. Dai, X., Liu, J., & Zhang, X. (2020). A review of studies applying machine learning models to predict occupancy and window-opening behaviours in smart buildings. Energy and Buildings, 223, 110159. Liu, J., Dai, X., Li, X., Jia, S., Pei, J., Sun, Y., Lai, D., Shen, X., Sun, H., Yin, H., … (2018). Indoor air quality and occupants’ ventilation habits in China: Seasonal measurement and long-term monitoring. Building and Environment, 142, 119–129. Dai, X., Huang, L., Qian, Y., Xia, S., Chong, W., Liu, J., Ieva, A. D., Hou, X., & Ou, C. (2020). Deep learning for automated cerebral aneurysm detection on computed tomography images. International Journal of Computer Assisted Radiology and Surgery, 15, 715–723. Dai, X., Liu, J., Li, X., & Zhao, L. (2018). Long-term monitoring of indoor CO2 and PM2.5 in Chinese homes: Concentrations and their relationships with outdoor environments. Building and Environment, 144, 238–247.
Featured Publications Cao, X., Dai, X., & Liu, J. (2016). Building energy-consumption status worldwide and the state-of-the-art technologies for zero-energy buildings during the past decade. Energy and Buildings, 128, 198–213. Dai, X., Liu, J., & Zhang, X. (2020). A review of studies applying machine learning models to predict occupancy and window-opening behaviours in smart buildings. Energy and Buildings, 223, 110159. Liu, J., Dai, X., Li, X., Jia, S., Pei, J., Sun, Y., Lai, D., Shen, X., Sun, H., Yin, H., … (2018). Indoor air quality and occupants’ ventilation habits in China: Seasonal measurement and long-term monitoring. Building and Environment, 142, 119–129. Dai, X., Huang, L., Qian, Y., Xia, S., Chong, W., Liu, J., Ieva, A. D., Hou, X., & Ou, C. (2020). Deep learning for automated cerebral aneurysm detection on computed tomography images. International Journal of Computer Assisted Radiology and Surgery, 15, 715–723. Dai, X., Liu, J., Li, X., & Zhao, L. (2018). Long-term monitoring of indoor CO2 and PM2.5 in Chinese homes: Concentrations and their relationships with outdoor environments. Building and Environment, 144, 238–247.
Featured Publications Cao, X., Dai, X., & Liu, J. (2016). Building energy-consumption status worldwide and the state-of-the-art technologies for zero-energy buildings during the past decade. Energy and Buildings, 128, 198–213. Dai, X., Liu, J., & Zhang, X. (2020). A review of studies applying machine learning models to predict occupancy and window-opening behaviours in smart buildings. Energy and Buildings, 223, 110159. Liu, J., Dai, X., Li, X., Jia, S., Pei, J., Sun, Y., Lai, D., Shen, X., Sun, H., Yin, H., … (2018). Indoor air quality and occupants’ ventilation habits in China: Seasonal measurement and long-term monitoring. Building and Environment, 142, 119–129. Dai, X., Huang, L., Qian, Y., Xia, S., Chong, W., Liu, J., Ieva, A. D., Hou, X., & Ou, C. (2020). Deep learning for automated cerebral aneurysm detection on computed tomography images. International Journal of Computer Assisted Radiology and Surgery, 15, 715–723. Dai, X., Liu, J., Li, X., & Zhao, L. (2018). Long-term monitoring of indoor CO2 and PM2.5 in Chinese homes: Concentrations and their relationships with outdoor environments. Building and Environment, 144, 238–247.
Featured Publications Cao, X., Dai, X., & Liu, J. (2016). Building energy-consumption status worldwide and the state-of-the-art technologies for zero-energy buildings during the past decade. Energy and Buildings, 128, 198–213. Dai, X., Liu, J., & Zhang, X. (2020). A review of studies applying machine learning models to predict occupancy and window-opening behaviours in smart buildings. Energy and Buildings, 223, 110159. Liu, J., Dai, X., Li, X., Jia, S., Pei, J., Sun, Y., Lai, D., Shen, X., Sun, H., Yin, H., … (2018). Indoor air quality and occupants’ ventilation habits in China: Seasonal measurement and long-term monitoring. Building and Environment, 142, 119–129. Dai, X., Huang, L., Qian, Y., Xia, S., Chong, W., Liu, J., Ieva, A. D., Hou, X., & Ou, C. (2020). Deep learning for automated cerebral aneurysm detection on computed tomography images. International Journal of Computer Assisted Radiology and Surgery, 15, 715–723. Dai, X., Liu, J., Li, X., & Zhao, L. (2018). Long-term monitoring of indoor CO2 and PM2.5 in Chinese homes: Concentrations and their relationships with outdoor environments. Building and Environment, 144, 238–247.
Featured Publications Cao, X., Dai, X., & Liu, J. (2016). Building energy-consumption status worldwide and the state-of-the-art technologies for zero-energy buildings during the past decade. Energy and Buildings, 128, 198–213. Dai, X., Liu, J., & Zhang, X. (2020). A review of studies applying machine learning models to predict occupancy and window-opening behaviours in smart buildings. Energy and Buildings, 223, 110159. Liu, J., Dai, X., Li, X., Jia, S., Pei, J., Sun, Y., Lai, D., Shen, X., Sun, H., Yin, H., … (2018). Indoor air quality and occupants’ ventilation habits in China: Seasonal measurement and long-term monitoring. Building and Environment, 142, 119–129. Dai, X., Huang, L., Qian, Y., Xia, S., Chong, W., Liu, J., Ieva, A. D., Hou, X., & Ou, C. (2020). Deep learning for automated cerebral aneurysm detection on computed tomography images. International Journal of Computer Assisted Radiology and Surgery, 15, 715–723. Dai, X., Liu, J., Li, X., & Zhao, L. (2018). Long-term monitoring of indoor CO2 and PM2.5 in Chinese homes: Concentrations and their relationships with outdoor environments. Building and Environment, 144, 238–247.
Featured Publications Cao, X., Dai, X., & Liu, J. (2016). Building energy-consumption status worldwide and the state-of-the-art technologies for zero-energy buildings during the past decade. Energy and Buildings, 128, 198–213. Dai, X., Liu, J., & Zhang, X. (2020). A review of studies applying machine learning models to predict occupancy and window-opening behaviours in smart buildings. Energy and Buildings, 223, 110159. Liu, J., Dai, X., Li, X., Jia, S., Pei, J., Sun, Y., Lai, D., Shen, X., Sun, H., Yin, H., … (2018). Indoor air quality and occupants’ ventilation habits in China: Seasonal measurement and long-term monitoring. Building and Environment, 142, 119–129. Dai, X., Huang, L., Qian, Y., Xia, S., Chong, W., Liu, J., Ieva, A. D., Hou, X., & Ou, C. (2020). Deep learning for automated cerebral aneurysm detection on computed tomography images. International Journal of Computer Assisted Radiology and Surgery, 15, 715–723. Dai, X., Liu, J., Li, X., & Zhao, L. (2018). Long-term monitoring of indoor CO2 and PM2.5 in Chinese homes: Concentrations and their relationships with outdoor environments. Building and Environment, 144, 238–247.
Featured Publications Cao, X., Dai, X., & Liu, J. (2016). Building energy-consumption status worldwide and the state-of-the-art technologies for zero-energy buildings during the past decade. Energy and Buildings, 128, 198–213. Dai, X., Liu, J., & Zhang, X. (2020). A review of studies applying machine learning models to predict occupancy and window-opening behaviours in smart buildings. Energy and Buildings, 223, 110159. Liu, J., Dai, X., Li, X., Jia, S., Pei, J., Sun, Y., Lai, D., Shen, X., Sun, H., Yin, H., … (2018). Indoor air quality and occupants’ ventilation habits in China: Seasonal measurement and long-term monitoring. Building and Environment, 142, 119–129. Dai, X., Huang, L., Qian, Y., Xia, S., Chong, W., Liu, J., Ieva, A. D., Hou, X., & Ou, C. (2020). Deep learning for automated cerebral aneurysm detection on computed tomography images. International Journal of Computer Assisted Radiology and Surgery, 15, 715–723. Dai, X., Liu, J., Li, X., & Zhao, L. (2018). Long-term monitoring of indoor CO2 and PM2.5 in Chinese homes: Concentrations and their relationships with outdoor environments. Building and Environment, 144, 238–247.
Featured Publications Cao, X., Dai, X., & Liu, J. (2016). Building energy-consumption status worldwide and the state-of-the-art technologies for zero-energy buildings during the past decade. Energy and Buildings, 128, 198–213. Dai, X., Liu, J., & Zhang, X. (2020). A review of studies applying machine learning models to predict occupancy and window-opening behaviours in smart buildings. Energy and Buildings, 223, 110159. Liu, J., Dai, X., Li, X., Jia, S., Pei, J., Sun, Y., Lai, D., Shen, X., Sun, H., Yin, H., … (2018). Indoor air quality and occupants’ ventilation habits in China: Seasonal measurement and long-term monitoring. Building and Environment, 142, 119–129. Dai, X., Huang, L., Qian, Y., Xia, S., Chong, W., Liu, J., Ieva, A. D., Hou, X., & Ou, C. (2020). Deep learning for automated cerebral aneurysm detection on computed tomography images. International Journal of Computer Assisted Radiology and Surgery, 15, 715–723. Dai, X., Liu, J., Li, X., & Zhao, L. (2018). Long-term monitoring of indoor CO2 and PM2.5 in Chinese homes: Concentrations and their relationships with outdoor environments. Building and Environment, 144, 238–247.
Prof. MUHAMMAD FAIZAN TAHIR | Engineering | Best Researcher Award Published on 22/05/202422/05/2024 by Environmental Scientists Prof. MUHAMMAD FAIZAN TAHIR | Engineering | Best Researcher Award Post-Doctoral Associate, New York University, United Arab Emirates Prof. Muhammad Faizan Tahir, an esteemed Post-Doctoral Associate at New York University, United Arab Emirates, has been honored with the Best Researcher Award in Engineering. 🏆 His groundbreaking contributions to the field are marked by innovative solutions and rigorous academic inquiry. With a keen focus on advancing knowledge and addressing pressing challenges, Prof. Tahir’s work exemplifies excellence in research. His dedication to pushing the boundaries of engineering science inspires peers and students alike. This accolade serves as a testament to his unwavering commitment to scholarly excellence and his significant impact on the academic community. 🎓 PROFILE Google Scholar , Scopus EDUCATION Prof. Muhammad Faizan Tahir’s academic journey is marked by stellar achievements. He earned his Ph.D. in Electrical Engineering from South China University of Technology, China, showcasing his commitment to scholarly pursuits. 🎓 Prior to this, he completed his MS in Electrical Engineering at The University of Lahore, Pakistan, where he honed his expertise. 📚 His educational foundation was laid at the University of Engineering and Technology, Taxila, Pakistan, where he obtained his BS in Electrical Engineering. 🏫 Prof. Tahir’s diverse educational background equips him with a comprehensive understanding of the field, enabling him to make significant contributions to research and academia. STRENGTHS Prof. Muhammad Faizan Tahir possesses a diverse skill set that encompasses both professional and technical domains. As a skilled communicator, he excels in public speaking and cross-cultural communication, fostering effective collaborations. 🗣️ Proficient in team and administration management, he serves as a motivator and leader, driving projects towards success. 💼 On the technical front, his expertise spans various areas including ensemble artificial neural networks and fully informed particle swarm optimization, enhancing problem-solving capabilities. 🧠 Prof. Tahir is adept in utilizing tools and technologies such as EnergyPLAN, HOMER, MATLAB, and MS Office, facilitating efficient project management and analysis. 🛠️ EXPERIENCE Prof. Muhammad Faizan Tahir’s illustrious career spans prestigious institutions across the globe. As a Post-Doctoral Associate at New York University Abu Dhabi, he pioneers research in sustainable energy technology, focusing on solar PV power forecasting and soiling effects on PV performance. 🌞 Previously, at South China University of Technology, he conducted groundbreaking research on integrated energy systems, showcasing his expertise in exergy analysis and renewable energy integration. 🌐 His journey also includes roles as a Research Scholar and Lecturer at the same institution and as a Lecturer and Lab Engineer at The University of Lahore, Pakistan, where he imparted knowledge in electrical engineering and conducted various labs. 🎓 PROJECTS Prof. Muhammad Faizan Tahir’s academic and research endeavors epitomize innovation and excellence. Currently serving as a Post-Doctoral Associate at New York University Abu Dhabi, UAE, he delves into solar PV power forecasting using machine learning and evaluates soiling effects on PV panel performance, contributing to sustainable energy solutions. ☀️ His prior role at South China University of Technology involved pioneering energy and exergy analysis of China’s integrated energy system, emphasizing renewable energy sources. 🌐 Prof. Tahir’s Ph.D. and MS theses focused on integrated energy system modeling and optimal load shedding, respectively, showcasing his expertise in renewable energy and AI. 🎓 His undergraduate project on intelligent dam monitoring further underscores his multidisciplinary approach to engineering challenges. 🌟 ACHIEVEMENTS Prof. Muhammad Faizan Tahir’s accolades and achievements reflect his commitment to excellence and continuous learning. He completed CITI courses and cybersecurity training, enhancing his expertise in research ethics and cybersecurity. 🛡️ His academic prowess earned him the Academic Excellence Award during his Ph.D. studies at SCUT, where he also received the prestigious CSC Scholarship. 🎓 Prof. Tahir’s outstanding academic performance was evident as he secured the 1st rank in MSEE with a remarkable CGPA. 🏅 Additionally, he demonstrated linguistic proficiency by passing the HSK-4 exam and shared his knowledge by delivering lectures on research methodology at SCUT. 📚 These accomplishments underscore his dedication to scholarly pursuits and knowledge dissemination. 🌟 PUBLICATIONS Top Notes Title: Short term electricity load forecasting using hybrid prophet-LSTM model optimized by BPNN Year: 2022 Citations: 122 Title: Integrated energy system modeling of China for 2020 by incorporating demand response, heat pump and thermal storage Year: 2019 Citations: 91 Title: A Novel Algorithm for MPPT of an Isolated PV System Using Push Pull Converter with Fuzzy Logic Controller Year: 2020 Citations: 68 Title: Optimizing size of variable renewable energy sources by incorporating energy storage and demand response Year: 2019 Citations: 64 Title: A comprehensive review of 4E analysis of thermal power plants, intermittent renewable energy and integrated energy systems Year: 2021 Citations: 59