What is he best known for?
The fields of study he is best known for:
- Artificial intelligence
- Electrical engineering
- Mechanical engineering
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 most cited work include:
- Development of prediction models for next-day building energy consumption and peak power demand using data mining techniques (254 citations)
- A short-term building cooling load prediction method using deep learning algorithms (242 citations)
- Quantitative energy performance assessment methods for existing buildings (196 citations)
What are the main themes of his work throughout his whole career to date?
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
- Automotive engineering which is related to area like Energy consumption, Thermal comfort, Demand response and Smart grid,
- Desiccant, which have a strong connection to Regenerative heat exchanger.
His Simulation research integrates issues from Energy performance, Reliability engineering, Relative humidity and TRNSYS.
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.
He most often published in these fields:
- Air conditioning (23.65%)
- Simulation (20.27%)
- Efficient energy use (16.22%)
What were the highlights of his more recent work (between 2017-2021)?
- Data analysis (6.76%)
- Building energy (7.43%)
- Air conditioning (23.65%)
In recent papers he was focusing on the following fields of study:
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.
Between 2017 and 2021, his most popular works were:
- Analytical investigation of autoencoder-based methods for unsupervised anomaly detection in building energy data (79 citations)
- Unsupervised data analytics in mining big building operational data for energy efficiency enhancement: A review (71 citations)
- Price-responsive model-based optimal demand response control of inverter air conditioners using genetic algorithm (35 citations)
In his most recent research, the most cited papers focused on:
- Artificial intelligence
- Electrical engineering
- Mechanical engineering
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.
