A hybrid data mining approach for anomaly detection and evaluation in residential buildings energy data-Reference-Cited by-同舟云学术

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A hybrid data mining approach for anomaly detection and evaluation in residential buildings energy data

Published:2020-05 Issue: Volume:215 Page:109864
ISSN:0378-7788
Container-title:Energy and Buildings
language:en
Short-container-title:Energy and Buildings

Author:

Xu Chengliang,Chen Huanxin

Funder

National Natural Science Foundation of China

Publisher

Elsevier BV

Subject

Electrical and Electronic Engineering,Mechanical Engineering,Building and Construction,Civil and Structural Engineering

Reference37 articles.

1. M. Ma, W. Cai, Y. Wu, China act on the energy efficiency of civil buildings (2008): a decade review, sci total environ, 651 (Pt 1) (2019) 42–60.

2. Comprehensive analysis of the relationship between thermal comfort and building control research - A data-driven literature review;Park;Renew. Sustain. Energ. Rev.,2018

3. Methodology of comprehensive building energy performance diagnosis for large commercial buildings at multiple levels;Wang;Appl Energ,2016

4. Fault detection analysis using data mining techniques for a cluster of smart office buildings;Capozzoli;Expert Syst. Appl.,2015

5. An ensemble learning framework for anomaly detection in building energy consumption;Araya;Energ. Buildings,2017

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