Exploring Urban Building Carbon Sinks: A SHAP-Driven Machine Learning Approach DOI
Angjian Wu, Zhitai Wang

Sustainable Cities and Society, Journal Year: 2025, Volume and Issue: unknown, P. 106428 - 106428

Published: May 1, 2025

Language: Английский

A proposal on a co-generation system accompanied with phase change material to supply energy demand of a hospital to make it a zero energy building (ZEB) DOI
Ehsanolah Assareh,

Abolfazl Keykhah,

Le Cao Nhien

et al.

Energy and Buildings, Journal Year: 2024, Volume and Issue: 318, P. 114478 - 114478

Published: June 28, 2024

Language: Английский

Citations

10
Energy consumption prediction and energy-saving suggestions of public buildings based on machine learning DOI
Cheng Chen, Zhiming Gao, Xuan Zhou

et al.

Energy and Buildings, Journal Year: 2024, Volume and Issue: 320, P. 114585 - 114585

Published: July 30, 2024

Language: Английский

Citations

8
Experimental and numerical study on heat transfer and energy storage characteristics in double-layered enclosure packed with microencapsulated phase change material DOI
Wei‐Mon Yan, Yanli Lin, Uzair Sajjad

et al.

International Journal of Heat and Fluid Flow, Journal Year: 2025, Volume and Issue: 112, P. 109757 - 109757

Published: Jan. 23, 2025

Language: Английский

Citations

1
A review on hybrid physics and data-driven modeling methods applied in air source heat pump systems for energy efficiency improvement DOI

Yanhua Guo,

Ningbo Wang,

Shuangquan Shao

et al.

Renewable and Sustainable Energy Reviews, Journal Year: 2024, Volume and Issue: 204, P. 114804 - 114804

Published: Aug. 14, 2024

Language: Английский

Citations

7
Comparing hyperparameter tuning methods in machine learning based urban building energy modeling: A study in Chicago DOI
Steven Jige Quan

Energy and Buildings, Journal Year: 2024, Volume and Issue: 317, P. 114353 - 114353

Published: May 26, 2024

Language: Английский

Citations

6
A Comparative Analysis of Machine Learning Algorithms in Predicting the Performance of a Combined Radiant Floor and Fan Coil Cooling System DOI Creative Commons

Shengze Lu,

Mengying Cui,

Bo Gao

et al.

Buildings, Journal Year: 2024, Volume and Issue: 14(6), P. 1659 - 1659

Published: June 4, 2024

Machine learning algorithms have proven to be practical in a wide range of applications. Many studies been conducted on the operational energy consumption and thermal comfort radiant floor systems. This paper conducts case study self-designed experimental setup that combines fan coil cooling (RFCFC) develops data monitoring system as source historical data. Seven machine (extreme (ELM), convolutional neural network (CNN), genetic algorithm-back propagation (GA-BP), radial basis function (RBF), random forest (RF), support vector (SVM), long short-term memory (LSTM)) were employed predict behavior RFCFC system. Corresponding prediction models then developed evaluate operative temperature (Top) (Eh). The performance model was evaluated using five error metrics. obtained results showed RF had very high predicting Top Eh, with correlation coefficients (>0.9915) low Compared other models, it also demonstrated accuracy Eh prediction, yielding maximum reductions 68.1, 82.4, 43.2% mean absolute percentage (MAPE), squared (MSE), (MAE), respectively. A sensitivity ranking algorithm analysis conducted. importance adjusting parameters, such supply water temperature, enhance indoor comfort. provides novel effective method for evaluating efficiency It insights optimizing systems, lays theoretical foundation future integrating this field.

Language: Английский

Citations

6
Tailored and Impactful Retrofit Guides for Swedish Building Stocks Using Big Data and Explainable Ai DOI
Santhan Reddy Penaka, Kailun Feng, Thomas Olofsson

et al.

Published: Jan. 1, 2025

As part of carbon neutrality goal, many Swedish municipalities have set local energy efficiency targets. Achieving these targets requires informed decision-making tailored to their contexts. Given that each municipality's building stock is characterized by specific conditions result in varying thermal performance terms U-values, making it important consider this inherent heterogeneity when designing retrofitting strategies. Tailored focus on identifying the most impactful features influencing and prioritizing planning pathways. Previously, has often been overlooked, resulting homogeneous modelling distinct buildings, thereby limiting ability provide retrofitting. However, with rise data-driven techniques increasing data availability (e.g., certificates), new approaches could be explored leverage buildings' big for insights.This study aims identify retrofit guides 81 groups across Linköping, Lund Umeå Sweden. To accomplish this, a framework introduced integrates explainable artificial intelligence an ensemble machine learning model. By leveraging heterogeneous data, contributing group are identified, providing strategies at These results support achieving efficient goals, approach provides general large-scale planning.

Language: Английский

Citations

0
Atlas for sustainable Egyptian governorates buildings based on wind/solar potential: Power, efficiency, economic, environmental, and thermal comfort maps DOI
Brian Senyonyi, Hatem Mahmoud, Hamdy Hassan

et al.

Energy and Buildings, Journal Year: 2025, Volume and Issue: unknown, P. 115314 - 115314

Published: Jan. 1, 2025

Language: Английский

Citations

0
Machine learning-aided biochar design for the adsorptive removal of emerging inorganic pollutants in water DOI
Habib Ullah,

Sangar Khan,

Xiaoying Zhu

et al.

Separation and Purification Technology, Journal Year: 2025, Volume and Issue: unknown, P. 131421 - 131421

Published: Jan. 1, 2025

Language: Английский

Citations

0
Analyzing different household energy use patterns using clustering and machine learning DOI
Xuerong Cui, Minhyun Lee, Mohammad Nyme Uddin

et al.

Renewable and Sustainable Energy Reviews, Journal Year: 2025, Volume and Issue: 212, P. 115335 - 115335

Published: Jan. 31, 2025

Language: Английский

Citations

0