A data analytics-based tool for the detection and diagnosis of anomalous daily energy patterns in buildings

Recognition and classification of typical load profiles in buildings with non-intrusive learning approach

(2019) Marco Savino Piscitelli et al.   APPLIED ENERGY

Analytical investigation of autoencoder-based methods for unsupervised anomaly detection in building energy data

(2018) Cheng Fan et al.   APPLIED ENERGY

Automated load pattern learning and anomaly detection for enhancing energy management in smart buildings

(2018) Alfonso Capozzoli et al.   ENERGY

A review of unsupervised statistical learning and visual analytics techniques applied to performance analysis of non-residential buildings

(2018) Clayton Miller et al.   RENEWABLE & SUSTAINABLE ENERGY REVIEWS

Building electric energy prediction modeling for BEMS using easily obtainable weather factors with Kriging model and data mining

(2018) Kibeom Ku et al.   Building Simulation

Optimal Selection of Clustering Algorithm via Multi-Criteria Decision Analysis (MCDA) for Load Profiling Applications

(2018) Ioannis Panapakidis et al.   Applied Sciences-Basel

Data mining based framework to identify rule based operation strategies for buildings with power metering system

(2018) Shunian Qiu et al.   Building Simulation

Cluster analysis for occupant-behavior based electricity load patterns in buildings: A case study in Shanghai residences

(2017) Song Pan et al.   Building Simulation

A review of fault detection and diagnostics methods for building systems

(2017) Woohyun Kim et al.   Science and Technology for the Built Environment

A sensor fault detection strategy for air handling units using cluster analysis

(2016) Rui Yan et al.   AUTOMATION IN CONSTRUCTION

Fault detection and diagnosis for building cooling system with a tree-structured learning method

(2016) Dan Li et al.   ENERGY AND BUILDINGS

Computational intelligence techniques for HVAC systems: A review

(2016) Muhammad Waseem Ahmad et al.   Building Simulation

Diagnostic Bayesian networks for diagnosing air handling units faults – Part II: Faults in coils and sensors

(2015) Yang Zhao et al.   APPLIED THERMAL ENGINEERING

Automated daily pattern filtering of measured building performance data

(2015) Clayton Miller et al.   AUTOMATION IN CONSTRUCTION

Temporal knowledge discovery in big BAS data for building energy management

(2015) Cheng Fan et al.   ENERGY AND BUILDINGS

Fault detection analysis using data mining techniques for a cluster of smart office buildings

(2015) Alfonso Capozzoli et al.   EXPERT SYSTEMS WITH APPLICATIONS

Real-time detection of anomalous power consumption

(2014) Jui-Sheng Chou et al.   RENEWABLE & SUSTAINABLE ENERGY REVIEWS

Fault detection and diagnosis for buildings and HVAC systems using combined neural networks and subtractive clustering analysis

(2013) Zhimin Du et al.   BUILDING AND ENVIRONMENT

Extracting knowledge from building-related data — A data mining framework

(2013) Zhun Yu et al.   Building Simulation

Automated FDD of multiple-simultaneous faults (MSF) and the application to building chillers

(2011) Hua Han et al.   ENERGY AND BUILDINGS

Important sensors for chiller fault detection and diagnosis (FDD) from the perspective of feature selection and machine learning

(2010) H. Han et al.   INTERNATIONAL JOURNAL OF REFRIGERATION-REVUE INTERNATIONALE DU FROID