A Novel Intelligent Scheme for Building Energy Prediction Based On Machine Learning and Deep Learning Algorithms DOI

M Jayashankara,

Prasenjit Chanak, Sanjay Kumar Singh

et al.

Published: Jan. 1, 2024

Language: Английский

Optimizing Smart Home Appliance Energy Monitoring using Factorial Hidden Markov Models for Internet of Behavior DOI
Siqi Liu,

Zhiyuan Xie,

Zheng-wei Hu

et al.

Journal of Building Engineering, Journal Year: 2024, Volume and Issue: unknown, P. 110732 - 110732

Published: Sept. 1, 2024

Language: Английский

Citations

1
Internet of things-based study on online monitoring system of building equipment energy saving optimization control using building information modeling DOI Creative Commons

Dinglong Xie,

Qiusha Xie

Science Progress, Journal Year: 2024, Volume and Issue: 107(2)

Published: April 1, 2024

Smart building equipment monitoring is a well-established field focused on enhancing contemporary comfort. The proliferation of Internet connectivity, facilitated by the internet things (IoT), has transformed buildings from static structures into interactive environments. IoT witnessed substantial growth across various aspects daily life, environmental conditions to managing systems and storing data in cloud. One critical application intelligent control equipment, such as air conditioners, optimize energy efficiency-a matter increasing concern for owners, design experts, system integrators. Achieving comprehensive savings demands meticulous approach energy-efficient control. This paper's primary objective explore analyze IoT-based energy-saving optimization techniques integrating information modeling (BIM) technology. It particularly delves conservation algorithm air-conditioning systems. research presents challenge rooted optimization, established upon specific functions, followed detailed explanation algorithm. To validate their approach, paper outlines experimental design. Over three sessions August, they conducted experiments two distinct areas. Area 1 implemented methodology discussed paper, utilizing virtual parameter enhancement mechanisms, while 2 adhered conventional methods. results were enlightening. demonstrated superior efficiency, consuming 735 kWh compared 2's 819 kWh, signifying an impressive 11.43% reduction consumption thanks optimized strategy. underscores practicality significance implementing strategies, with focus smart thermostats, HVAC controllers, daylight sensors, management achieve gains.

Language: Английский

Citations

1
Data-driven Technology Applications in Planning, Demand-side Management, and Cybersecurity for Smart Household Community DOI
Dipanshu Naware, Arghya Mitra

IEEE Transactions on Artificial Intelligence, Journal Year: 2024, Volume and Issue: 5(10), P. 4868 - 4883

Published: June 20, 2024

Language: Английский

Citations

1
Intelligent learning approaches for demand-side controller for BIPV-integrated buildings DOI
Zhengxuan Liu, Linfeng Zhang, Shaojun Wang

et al.

Elsevier eBooks, Journal Year: 2024, Volume and Issue: unknown, P. 205 - 233

Published: Jan. 1, 2024

Language: Английский

Citations

0
A Novel Intelligent Scheme for Building Energy Prediction Based On Machine Learning and Deep Learning Algorithms DOI

M Jayashankara,

Prasenjit Chanak, Sanjay Kumar Singh

et al.

Published: Jan. 1, 2024

Language: Английский

Citations

0