Artificial intelligence, Data mining, Efficient energy use, Fault detection and isolation and Principal component analysis are his primary areas of study. He has included themes like Field and Machine learning in his Artificial intelligence study. His study in Data mining is interdisciplinary in nature, drawing from both Building automation, Cluster analysis and Feature extraction.
His biological study spans a wide range of topics, including Humidity, Desiccant, Process engineering and Dedicated outdoor air system. His research integrates issues of Real-time computing, Electronic engineering and HVAC in his study of Fault detection and isolation. His research in Principal component analysis intersects with topics in Simulation and Robustness.
His scientific interests lie mostly in Air conditioning, Simulation, Efficient energy use, Fault detection and isolation and Chiller. His Air conditioning study also includes fields such as
His work deals with themes such as Principal component analysis, Support vector machine, Robustness and Artificial intelligence, which intersect with Fault detection and isolation. In his research on the topic of Artificial intelligence, Cluster analysis and Feature extraction is strongly related with Data mining. His work carried out in the field of Chiller brings together such families of science as Control theory, Cooling load, Water cooling, Control engineering and Sensor fusion.
Fu Xiao focuses on Data analysis, Building energy, Air conditioning, Efficient energy use and Big data. His Data analysis study combines topics in areas such as Anomaly detection, Knowledge extraction and Data science. His Air conditioning study incorporates themes from Controllability, Control theory, Cooling capacity, Automotive engineering and PID controller.
His studies in Efficient energy use integrate themes in fields like Energy consumption, Uncertainty analysis, Smart grid and Renewable energy. The concepts of his Big data study are interwoven with issues in Data-driven, Database, Artificial intelligence and Building management system. His Artificial intelligence research incorporates elements of Field, Machine learning and Computational fluid dynamics.
Fu Xiao mainly investigates Demand response, Building energy, Data analysis, Automotive engineering and Electricity. His Demand response research is multidisciplinary, incorporating perspectives in Peak demand and Thermal mass. As part of one scientific family, he deals mainly with the area of Thermal mass, narrowing it down to issues related to the Energy consumption, and often Efficient energy use.
His Building energy research includes themes of Quality, Decision tree, Cluster analysis, Association rule learning and Architectural engineering. The various areas that Fu Xiao examines in his Data analysis study include Knowledge extraction and Data science. His Automotive engineering research incorporates themes from Model predictive control, Smart grid and Air conditioning.
This overview was generated by a machine learning system which analysed the scientist’s body of work. If you have any feedback, you can contact us here.
Development of prediction models for next-day building energy consumption and peak power demand using data mining techniques
Cheng Fan;Fu Xiao;Shengwei Wang.
Applied Energy (2014)
Quantitative energy performance assessment methods for existing buildings
Shengwei Wang;Chengchu Yan;Fu Xiao.
Energy and Buildings (2012)
A short-term building cooling load prediction method using deep learning algorithms
Cheng Fan;Fu Xiao;Yang Zhao.
Applied Energy (2017)
AHU sensor fault diagnosis using principal component analysis method
Shengwei Wang;Fu Xiao.
Energy and Buildings (2004)
Peak load shifting control using different cold thermal energy storage facilities in commercial buildings: A review
Yongjun Sun;Shengwei Wang;Fu Xiao;Diance Gao.
Energy Conversion and Management (2013)
Data mining in building automation system for improving building operational performance
Fu Xiao;Cheng Fan.
Energy and Buildings (2014)
A framework for knowledge discovery in massive building automation data and its application in building diagnostics
Cheng Fan;Fu Xiao;Chengchu Yan.
Automation in Construction (2015)
Research and application of evaporative cooling in China: A review (I) – Research
Y.M. Xuan;Y.M. Xuan;F. Xiao;X.F. Niu;X.F. Niu;X. Huang.
Renewable & Sustainable Energy Reviews (2012)
Pattern recognition-based chillers fault detection method using Support Vector Data Description (SVDD)
Yang Zhao;Shengwei Wang;Fu Xiao.
Applied Energy (2013)
An intelligent chiller fault detection and diagnosis methodology using Bayesian belief network
Yang Zhao;Fu Xiao;Shengwei Wang.
Energy and Buildings (2013)
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