相关参考文献
注意：仅列出部分参考文献，下载原文获取全部文献信息。A data analytics-based tool for the detection and diagnosis of anomalous daily energy patterns in buildings
Marco Savino Piscitelli et al.
BUILDING SIMULATION (2021)
Statistical characterization of semi-supervised neural networks for fault detection and diagnosis of air handling units
Cheng Fan et al.
ENERGY AND BUILDINGS (2021)
Advanced data analytics for enhancing building performances: From data-driven to big data-driven approaches
Cheng Fan et al.
BUILDING SIMULATION (2021)
Data augmentation in fault diagnosis based on the Wasserstein generative adversarial network with gradient penalty
Xin Gao et al.
NEUROCOMPUTING (2020)
Statistical investigations of transfer learning-based methodology for short-term building energy predictions
Cheng Fan et al.
APPLIED ENERGY (2020)
Systematic approach to provide building occupants with feedback to reduce energy consumption
Milad Ashouri et al.
ENERGY (2020)
A baseline regularization scheme for transfer learning with convolutional neural networks
Xuhong Li et al.
PATTERN RECOGNITION (2020)
A new strategy to benchmark and evaluate building electricity usage using multiple data mining technologies
Kehua Li et al.
SUSTAINABLE ENERGY TECHNOLOGIES AND ASSESSMENTS (2020)
Enhancing operational performance of AHUs through an advanced fault detection and diagnosis process based on temporal association and decision rules
Marco Savino Piscitelli et al.
ENERGY AND BUILDINGS (2020)
Assessment of deep recurrent neural network-based strategies for short-term building energy predictions
Cheng Fan et al.
APPLIED ENERGY (2019)
Data driven parallel prediction of building energy consumption using generative adversarial nets
Chenlu Tian et al.
ENERGY AND BUILDINGS (2019)
Deep learning-based feature engineering methods for improved building energy prediction
Cheng Fan et al.
APPLIED ENERGY (2019)
A novel deep reinforcement learning based methodology for short-term HVAC system energy consumption prediction
Tao Liu et al.
INTERNATIONAL JOURNAL OF REFRIGERATION-REVUE INTERNATIONALE DU FROID (2019)
Times-series data augmentation and deep learning for construction equipment activity recognition
Khandakar M. Rashid et al.
ADVANCED ENGINEERING INFORMATICS (2019)
A graph mining-based methodology for discovering and visualizing high-level knowledge for building energy management
Cheng Fan et al.
APPLIED ENERGY (2019)
A data-driven approach to extract operational signatures of HVAC systems and analyze impact on electricity consumption
Xinran Yu et al.
APPLIED ENERGY (2019)
Imputing missing value through ensemble concept based on statistical measures
Moslem Mohammadi Jenghara et al.
KNOWLEDGE AND INFORMATION SYSTEMS (2018)
Development of building energy saving advisory: A data mining approach
Milad Ashouri et al.
ENERGY AND BUILDINGS (2018)
Transfer learning with seasonal and trend adjustment for cross-building energy forecasting
Mauro Ribeiro et al.
ENERGY AND BUILDINGS (2018)
Energy diagnosis of variable refrigerant flow (VRF) systems: Data mining technique and statistical quality control approach
Jiangyan Liu et al.
ENERGY AND BUILDINGS (2018)
On the feature engineering of building energy data mining
Chuan Zhang et al.
SUSTAINABLE CITIES AND SOCIETY (2018)
Semi-supervised learning for early detection and diagnosis of various air handling unit faults
Ke Yan et al.
ENERGY AND BUILDINGS (2018)
Mining big building operational data for improving building energy efficiency: A case study
Cheng Fan et al.
BUILDING SERVICES ENGINEERING RESEARCH & TECHNOLOGY (2018)
In-Depth Analysis of Energy Efficiency Related Factors in Commercial Buildings Using Data Cube and Association Rule Mining
Byeongjoon Noh et al.
SUSTAINABILITY (2017)
Data partitioning and association mining for identifying VRF energy consumption patterns under various part loads and refrigerant charge conditions
Guannan Li et al.
APPLIED ENERGY (2017)
A short-term building cooling load prediction method using deep learning algorithms
Cheng Fan et al.
APPLIED ENERGY (2017)
Forecasting electric demand of supply fan using data mining techniques
M. Le Cam et al.
ENERGY (2016)
Artificial neural network model for forecasting sub-hourly electricity usage in commercial buildings
Young Tae Chae et al.
ENERGY AND BUILDINGS (2016)
Benchmarking whole-building energy performance with multi-criteria technique for order preference by similarity to ideal solution using a selective objective-weighting approach
Endong Wang
APPLIED ENERGY (2015)
A framework for knowledge discovery in massive building automation data and its application in building diagnostics
Cheng Fan et al.
AUTOMATION IN CONSTRUCTION (2015)
Temporal knowledge discovery in big BAS data for building energy management
Cheng Fan et al.
ENERGY AND BUILDINGS (2015)
Fault detection analysis using data mining techniques for a cluster of smart office buildings
Alfonso Capozzoli et al.
EXPERT SYSTEMS WITH APPLICATIONS (2015)
Self-labeled techniques for semi-supervised learning: taxonomy, software and empirical study
Isaac Triguero et al.
KNOWLEDGE AND INFORMATION SYSTEMS (2015)
Development of prediction models for next-day building energy consumption and peak power demand using data Mining techniques
Cheng Fan et al.
APPLIED ENERGY (2014)
Data mining in building automation system for improving building operational performance
Fu Xiao et al.
ENERGY AND BUILDINGS (2014)
Smart meter monitoring and data mining techniques for predicting refrigeration system performance
Jui-Sheng Chou et al.
EXPERT SYSTEMS WITH APPLICATIONS (2014)
A predictive deviance criterion for selecting a generative model in semi-supervised classification
Vincent Vandewalle et al.
COMPUTATIONAL STATISTICS & DATA ANALYSIS (2013)
Locally linear reconstruction based missing value imputation for supervised learning
Pilsung Kang
NEUROCOMPUTING (2013)
A novel methodology for knowledge discovery through mining associations between building operational data
Zhun (Jerry) Yu et al.
ENERGY AND BUILDINGS (2012)
A multiple kernel framework for inductive semi-supervised SVM learning
Xilan Tian et al.
NEUROCOMPUTING (2012)
Modeling and optimization of HVAC energy consumption
Andrew Kusiak et al.
APPLIED ENERGY (2010)
Life cycle energy analysis of buildings: An overview
T. Ramesh et al.
ENERGY AND BUILDINGS (2010)
Using intelligent data analysis to detect abnormal energy consumption in buildings
John E. Seem
ENERGY AND BUILDINGS (2007)
分享论文
