Attention-based interpretable neural network for building cooling load prediction DOI
Ao Li, Fu Xiao, Chong Zhang

et al.

Applied Energy, Journal Year: 2021, Volume and Issue: 299, P. 117238 - 117238

Published: June 25, 2021

Language: Английский

A review of data-driven building energy consumption prediction studies DOI
Kadir Amasyali,

Nora El-Gohary

Renewable and Sustainable Energy Reviews, Journal Year: 2017, Volume and Issue: 81, P. 1192 - 1205

Published: Sept. 12, 2017

Language: Английский

Citations

1451
Trees vs Neurons: Comparison between random forest and ANN for high-resolution prediction of building energy consumption DOI Creative Commons
Muhammad Waseem Ahmad, Monjur Mourshed, Yacine Rezgui

et al.

Energy and Buildings, Journal Year: 2017, Volume and Issue: 147, P. 77 - 89

Published: April 22, 2017

Energy prediction models are used in buildings as a performance evaluation engine advanced control and optimisation, making informed decisions by facility managers utilities for enhanced energy efficiency. Simplified data-driven often the preferred option where pertinent information detailed simulation not available fast responses required. We compared of widely-used feed-forward back-propagation artificial neural network (ANN) with random forest (RF), an ensemble-based method gaining popularity – predicting hourly HVAC consumption hotel Madrid, Spain. Incorporating social parameters such numbers guests marginally increased accuracy both cases. Overall, ANN performed better than RF root-mean-square error (RMSE) 4.97 6.10 respectively. However, ease tuning modelling categorical variables offers algorithms advantage dealing multi-dimensional complex data, typical buildings. performs internal cross-validation (i.e. using out-of-bag samples) only has few parameters. Both have comparable predictive power nearly equally applicable building applications.

Language: Английский

Citations

828
Reinforcement learning for demand response: A review of algorithms and modeling techniques DOI
José R. Vázquez-Canteli, Zoltán Nagy

Applied Energy, Journal Year: 2018, Volume and Issue: 235, P. 1072 - 1089

Published: Nov. 17, 2018

Language: Английский

Citations

616
A short-term building cooling load prediction method using deep learning algorithms DOI
Cheng Fan, Fu Xiao, Yang Zhao

et al.

Applied Energy, Journal Year: 2017, Volume and Issue: 195, P. 222 - 233

Published: March 18, 2017

Language: Английский

Citations

596
A review of artificial intelligence based building energy use prediction: Contrasting the capabilities of single and ensemble prediction models DOI
Zeyu Wang, Ravi Srinivasan

Renewable and Sustainable Energy Reviews, Journal Year: 2016, Volume and Issue: 75, P. 796 - 808

Published: Nov. 10, 2016

Language: Английский

Citations

562
Model Predictive Control (MPC) for Enhancing Building and HVAC System Energy Efficiency: Problem Formulation, Applications and Opportunities DOI Creative Commons
Gianluca Serale, Massimo Fiorentini, Alfonso Capozzoli

et al.

Energies, Journal Year: 2018, Volume and Issue: 11(3), P. 631 - 631

Published: March 12, 2018

In the last few years, application of Model Predictive Control (MPC) for energy management in buildings has received significant attention from research community. MPC is becoming more and viable because increase computational power building automation systems availability a amount monitored data. found successful implementation thermal regulation, fully exploiting potential mass. Moreover, been positively applied to active storage systems, as well optimal on-site renewable sources. also opens up several opportunities enhancing efficiency operation Heating Ventilation Air Conditioning (HVAC) its ability consider constraints, prediction disturbances multiple conflicting objectives, such indoor comfort demand. Despite algorithms control thoroughly investigated various works, unified framework that describes formulates still lacking. Firstly, this work introduces common dictionary taxonomy gives ground all engineering disciplines involved design control. Secondly main scope paper define formulation critically discuss outcomes different existing HVAC system management. The benefits improving were highlighted.

Language: Английский

Citations

477
A review of machine learning in building load prediction DOI Creative Commons
Liang Zhang, Jin Wen, Yanfei Li

et al.

Applied Energy, Journal Year: 2021, Volume and Issue: 285, P. 116452 - 116452

Published: Jan. 13, 2021

Language: Английский

Citations

448
Building thermal load prediction through shallow machine learning and deep learning DOI Creative Commons
Zhe Wang, Tianzhen Hong,

Mary Ann Piette

et al.

Applied Energy, Journal Year: 2020, Volume and Issue: 263, P. 114683 - 114683

Published: Feb. 20, 2020

Language: Английский

Citations

343
Modeling techniques used in building HVAC control systems: A review DOI
Zakia Afroz, GM Shafiullah, Tania Urmee

et al.

Renewable and Sustainable Energy Reviews, Journal Year: 2017, Volume and Issue: 83, P. 64 - 84

Published: Dec. 12, 2017

Language: Английский

Citations

333
A review on optimization methods applied in energy-efficient building geometry and envelope design DOI
Farshad Kheiri

Renewable and Sustainable Energy Reviews, Journal Year: 2018, Volume and Issue: 92, P. 897 - 920

Published: May 17, 2018

Language: Английский

Citations

333