Generative pre-trained transformers (GPT)-based automated data mining for building energy management: Advantages, limitations and the future

[1]  Kunming Cheng,et al.  The Potential of GPT-4 as an AI-Powered Virtual Assistant for Surgeons Specialized in Joint Arthroplasty , 2023, Annals of Biomedical Engineering.

[2]  Jinyue Yan,et al.  Interpretable machine learning for building energy management: A state-of-the-art review , 2023, Advances in Applied Energy.

[3]  Xuejun Zhang,et al.  Causal discovery-based external attention in neural networks for accurate and reliable fault detection and diagnosis of building energy systems , 2022, Building and Environment.

[4]  Huanxin Chen,et al.  A Robust VRF fault diagnosis method based on ensemble BiLSTM with attention mechanism: Considering uncertainties and generalization , 2022, Energy and Buildings.

[5]  Long Gao,et al.  Enhanced chiller faults detection and isolation method based on independent component analysis and k-nearest neighbors classifier , 2022, Building and Environment.

[6]  Yang Zhao,et al.  Elastic weight consolidation-based adaptive neural networks for dynamic building energy load prediction modeling , 2022, Energy and Buildings.

[7]  Ke Yan,et al.  Chiller faults detection and diagnosis with sensor network and adaptive 1D CNN , 2022, Digit. Commun. Networks.

[8]  Long Gao,et al.  Fault detection and diagnosis using tree-based ensemble learning methods and multivariate control charts for centrifugal chillers , 2022, Journal of Building Engineering.

[9]  Tingting Li,et al.  A real-time abnormal operation pattern detection method for building energy systems based on association rule bases , 2021, Building Simulation.

[10]  Yu Hao,et al.  Optimization of group control strategy and analysis of energy saving in refrigeration plant , 2021 .

[11]  Donghui Li,et al.  Sensor drift fault diagnosis for chiller system using deep recurrent canonical correlation analysis and k-nearest neighbor classifier. , 2021, ISA transactions.

[12]  Yang Zhao,et al.  A knowledge-guided and data-driven method for building HVAC systems fault diagnosis , 2021, Building and Environment.

[13]  Yang Zhao,et al.  A comprehensive investigation of knowledge discovered from historical operational data of a typical building energy system , 2021 .

[14]  Sicheng Zhan,et al.  Building occupancy and energy consumption: Case studies across building types , 2021 .

[15]  Hyuncheol Seo,et al.  A data-driven fault detection and diagnosis scheme for air handling units in building HVAC systems considering undefined states , 2021, Journal of Building Engineering.

[16]  Jin Wen,et al.  A review of machine learning in building load prediction , 2021 .

[17]  Ao Li,et al.  Advanced data analytics for enhancing building performances: From data-driven to big data-driven approaches , 2020, Building Simulation.

[18]  David P. Yuill,et al.  Data-driven fault detection and diagnosis for packaged rooftop units using statistical machine learning classification methods , 2020 .

[19]  Xin Ma,et al.  Short-term prediction of building energy consumption employing an improved extreme gradient boosting model: A case study of an intake tower , 2020 .

[20]  Yang Zhao,et al.  A generic prediction interval estimation method for quantifying the uncertainties in ultra-short-term building cooling load prediction , 2020 .

[21]  Zihao Wang,et al.  A review of data mining technologies in building energy systems: Load prediction, pattern identification, fault detection and diagnosis , 2020 .

[22]  Michael Short,et al.  Electricity demand forecasting for decentralised energy management , 2020 .

[23]  Yang Zhao,et al.  An improved association rule mining-based method for revealing operational problems of building heating, ventilation and air conditioning (HVAC) systems , 2019, Applied Energy.

[24]  K. Poolla,et al.  EnergyStar++: Towards more accurate and explanatory building energy benchmarking , 2019, Applied Energy.

[25]  Ziwei Li,et al.  An ANN-based fast building energy consumption prediction method for complex architectural form at the early design stage , 2019, Building Simulation.

[26]  Yang Zhao,et al.  Artificial intelligence-based fault detection and diagnosis methods for building energy systems: Advantages, challenges and the future , 2019, Renewable and Sustainable Energy Reviews.

[27]  Hongjie Jia,et al.  Vector field-based support vector regression for building energy consumption prediction , 2019, Applied Energy.

[28]  Yue Yuan,et al.  An expert rule-based fault diagnosis strategy for variable refrigerant flow air conditioning systems , 2019, Applied Thermal Engineering.

[29]  Fu Xiao,et al.  A novel methodology to explain and evaluate data-driven building energy performance models based on interpretable machine learning , 2019, Applied Energy.

[30]  Jin Wen,et al.  A systematic feature selection procedure for short-term data-driven building energy forecasting model development , 2019, Energy and Buildings.

[31]  Guannan Li,et al.  Improved sensor fault detection, diagnosis and estimation for screw chillers using density-based clustering and principal component analysis , 2018, Energy and Buildings.

[32]  Zeyu Wang,et al.  Random Forest based hourly building energy prediction , 2018, Energy and Buildings.

[33]  Tanveer Ahmad,et al.  Short and medium-term forecasting of cooling and heating load demand in building environment with data-mining based approaches , 2018 .

[34]  Fu Xiao,et al.  Unsupervised data analytics in mining big building operational data for energy efficiency enhancement: A review , 2018 .

[35]  Huanxin Chen,et al.  A hybrid ICA-BPNN-based FDD strategy for refrigerant charge faults in variable refrigerant flow system , 2017 .

[36]  Qiang Zhang,et al.  Research on short-term and ultra-short-term cooling load prediction models for office buildings , 2017 .

[37]  Fu Xiao,et al.  A short-term building cooling load prediction method using deep learning algorithms , 2017 .

[38]  Zhenjun Ma,et al.  A decision tree based data-driven diagnostic strategy for air handling units , 2016 .

[39]  Jiong Li,et al.  A novel efficient SVM-based fault diagnosis method for multi-split air conditioning system's refrigerant charge fault amount , 2016 .

[40]  Fu Xiao,et al.  A framework for knowledge discovery in massive building automation data and its application in building diagnostics , 2015 .

[41]  Shengwei Wang,et al.  Development of prediction models for next-day building energy consumption and peak power demand using data mining techniques , 2014 .

[42]  F. Haghighat,et al.  Extracting knowledge from building-related data — A data mining framework , 2013 .

[43]  Hans P. Geering,et al.  Fault diagnosis for heat pumps with parameter identification and clustering , 2006 .

[44]  Srinivas Katipamula,et al.  Review Article: Methods for Fault Detection, Diagnostics, and Prognostics for Building Systems—A Review, Part I , 2005 .

[45]  Eckhard A. Groll,et al.  The Sensitivity of Chiller Performance to Common Faults , 2001 .

[46]  Mohammad Mehedi Hassan,et al.  An Ensemble Learning Approach for Accurate Energy Load Prediction in Residential Buildings , 2019, IEEE Access.