Energy Management in Modern Buildings Based on Demand Prediction and Machine Learning-A Review

被引：8

作者：

Moghimi, Seyed Morteza ^{[1
]}

Gulliver, Thomas Aaron ^{[1
]}

Thirumai Chelvan, Ilamparithi ^{[1
]}

机构：

[1] Univ Victoria, Dept Elect & Comp Engn, STN CSC, POB 1700, Victoria, BC V8W 2Y2, Canada

来源：

ENERGIES | 2024年 / 17卷 / 03期

基金：

英国科研创新办公室;

关键词：

demand response; energy flexibility; green buildings; machine learning; optimization; smart buildings; SMART BUILDINGS; DATA-DRIVEN; LOAD; CONSUMPTION; SYSTEM; CLASSIFICATION; REGRESSION; NETWORK;

D O I：

10.3390/en17030555

中图分类号：

TE [石油、天然气工业]; TK [能源与动力工程];

学科分类号：

0807 ; 0820 ;

摘要：

Increasing building energy consumption has led to environmental and economic issues. Energy demand prediction (DP) aims to reduce energy use. Machine learning (ML) methods have been used to improve building energy consumption, but not all have performed well in terms of accuracy and efficiency. In this paper, these methods are examined and evaluated for modern building (MB) DP.

引用

页数：20

共 101 条

[71] The Role of Machine Learning and the Internet of Things in Smart Buildings for Energy Efficiency [J].

Shah, Syed Faisal Abbas ;

Iqbal, Muhammad ;

Aziz, Zeeshan ;

Rana, Toqir A. ;

Khalid, Adnan ;

Cheah, Yu-N ;

Arif, Muhammad .

APPLIED SCIENCES-BASEL, 2022, 12 (15)

[72] Prediction of EV Charging Behavior Using Machine Learning [J].

Shahriar, Sakib ;

Al-Ali, A. R. ;

Osman, Ahmed H. ;

Dhou, Salam ;

Nijim, Mais .

IEEE ACCESS, 2021, 9 :111576-111586

[73]

Shen Wen, 2013, P INT C FUT EN SYST

[74] How can solid biomass contribute to the EU's renewable energy targets in 2020, 2030 and what are the GHG drivers and safeguards in energy- and forestry sectors? [J].

Sikkema, Richard ;

Proskurina, Svetlana ;

Banja, Manjola ;

Vakkilainen, Esa .

RENEWABLE ENERGY, 2021, 165 :758-772

[75]

Sinopoli J., 2008, Energy Eng, V105, P17, DOI [10.1080/01998590809509394, DOI 10.1080/01998590809509394]

[76] Demand response: a strategy to address residential air-conditioning peak load in Australia [J].

Smith, Robert ;

Meng, Ke ;

Dong, Zhaoyang ;

Simpson, Robert .

JOURNAL OF MODERN POWER SYSTEMS AND CLEAN ENERGY, 2013, 1 (03) :223-230

[77] Engaging with energy reduction: Does a climate change frame have the potential for achieving broader sustainable behaviour? [J].

Spence, A. ;

Leygue, C. ;

Bedwell, B. ;

O'Malley, C. .

JOURNAL OF ENVIRONMENTAL PSYCHOLOGY, 2014, 38 :17-28

[78] Model predictive control-based power dispatch for distribution system considering plug-in electric vehicle uncertainty [J].

Su, Wencong ;

Wang, Jianhui ;

Zhang, Kuilin ;

Huang, Alex Q. .

ELECTRIC POWER SYSTEMS RESEARCH, 2014, 106 :29-35

[79] COVID-19 Pandemic Prediction and Forecasting Using Machine Learning Classifiers [J].

Sultana, Jabeen ;

Singha, Anjani Kumar ;

Siddiqui, Shams Tabrez ;

Nagalaxmi, Guthikonda ;

Sriram, Anil Kumar ;

Pathak, Nitish .

INTELLIGENT AUTOMATION AND SOFT COMPUTING, 2022, 32 (02) :1007-1024

[80] Household-Level Energy Forecasting in Smart Buildings Using a Novel Hybrid Deep Learning Model [J].

Syed, Dabeeruddin ;

Abu-Rub, Haitham ;

Ghrayeb, Ali ;

Refaat, Shady S. .

IEEE ACCESS, 2021, 9 :33498-33511

← 2 3 4 5 6 7 8 9 10 11 →