Cited by A hybrid ensemble learning framework for zero-energy potential prediction of photovoltaic direct-driven air conditioners

Optimizing the View Percentage, Daylight Autonomy, Sunlight Exposure, and Energy Use: Data-Driven-Based Approach for Maximum Space Utilization in Residential Building Stock in Hot Climates DOI

Tarek M. Kamel, Amany Khalil, Mohammed M. Lakousha

et al.

Energies, Journal Year: 2024, Volume and Issue: 17(3), P. 684 - 684

Published: Jan. 31, 2024

This paper introduces a comprehensive methodology for creating diverse layout generation configurations, aiming to address limitations in existing building optimization studies that rely on simplistic hypothetical buildings. study’s objective was achieve an optimal balance between minimizing the energy use intensity (EUI) kWh/m2, maximizing views percentages outdoor (VPO), achieving spatial daylight autonomy (sDA), and annual sunlight exposure (ASE). To ensure accuracy reliability of simulation, research included calibration validation processes using Ladybug Honeybee plugins, integrated into Grasshopper platform. These involved comparing model’s performance against real-world case. Through more than 1500 iterations, study extracted three multi-regression equations enabled calculation EUI kWh/m2. demonstrated significant influence window-to-wall ratio (WWR) space proportions (SP) EUI. By utilizing these equations, we were able fine-tune design process, pinpoint make informed decisions minimize consumption enhance sustainability residential buildings hot arid climates. The findings indicated 61% variability can be attributed changes WWR, as highlighted first equation. Meanwhile, second equation suggested around 27% explained by alterations proportions, indicating moderate correlation. Lastly, third approximately 89% associated with SP pointing strong correlation SP, consumption. proposed method is flexible include new objectives variables future applications.

Language: Английский

Citations

A comprehensive benchmark of machine learning-based algorithms for medium-term electric vehicle charging demand prediction DOI

Ömer Can Tolun, Kasım Zor, Önder Tutsoy

et al.

The Journal of Supercomputing, Journal Year: 2025, Volume and Issue: 81(3)

Published: Feb. 10, 2025

Language: Английский

Citations

Adaptive thermal load prediction in residential buildings using artificial neural networks DOI

Mohammad Hossein Fouladfar, A. Soppelsa, Himanshu Nagpal

et al.

Journal of Building Engineering, Journal Year: 2023, Volume and Issue: 77, P. 107464 - 107464

Published: July 29, 2023

Accurate prediction of thermal load in buildings is essential for efficient energy planning. In this study, we investigate the application Artificial Neural Networks (ANNs) to predict and indoor temperature evolution residential buildings. We propose a flexible adaptive model that retrained on daily basis deliver updated hour-by-hour predictions following 24 hours. To strike balance between accuracy computational efficiency, employ various design choices, including feature selection using Pearson's correlation coefficient (PCC), dynamic architecture, down-sampling, specific state resetting weight initialization. The results demonstrate efficacy our proposed model, as indicated by low root mean square error (RMSE) bias (MBE) values. For zone temperature, average RMSE MBE are 0.3 °C 0.18 (summer) 0.5 0.2 (winter), respectively. Furthermore, 12 W/m2 -2.4 (winter) 10 -0.6 (summer), These performance metrics establish valuable tool optimizing heating cooling systems, resulting savings cost reductions. Our findings emphasize potential ANNs precise predictions, offering practical implications building operators, engineers, researchers involved management.

Language: Английский

Citations

Investigation of influential variations among variables in daylighting glare metrics using machine learning and SHAP DOI

Zhaoyang Luo,

Xuanning Qi,

C. T. Sun

et al.

Building and Environment, Journal Year: 2024, Volume and Issue: 254, P. 111394 - 111394

Published: March 7, 2024

Language: Английский

Citations

Deciphering the nonlinear and synergistic role of building energy variables in shaping carbon emissions: A LightGBM- SHAP framework in office buildings DOI

Congyue Zhou,

Zhu Wang,

Xuanli Wang

et al.

Building and Environment, Journal Year: 2024, Volume and Issue: 266, P. 112035 - 112035

Published: Aug. 31, 2024

Language: Английский

Citations

Evolving multi-objective optimization framework for early-stage building design: Improving energy efficiency, daylighting, view quality, and thermal comfort DOI

Lingrui Li,

Zongxin Qi,

Qingsong Ma

et al.

Building Simulation, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 16, 2024

Language: Английский

Citations

Quantifying potential dynamic façade energy savings in early design using constrained optimization DOI

Laura Hinkle, Julian Wang,

Nathan C. Brown

et al.

Building and Environment, Journal Year: 2022, Volume and Issue: 221, P. 109265 - 109265

Published: June 11, 2022

Language: Английский

Citations

Machine-learned kinetic Façade: Construction and artificial intelligence enabled predictive control for visual comfort DOI

Mollaeiubli Takhmasib,

Hyuk Jae Lee,

Hwang Yi

et al.

Automation in Construction, Journal Year: 2023, Volume and Issue: 156, P. 105093 - 105093

Published: Sept. 16, 2023

Language: Английский

Citations

Optimization of an office building form using a lattice incubate boxes method DOI

Amany Khalil,

Osama Tolba,

Sherif Ezzeldin

et al.

Advanced Engineering Informatics, Journal Year: 2023, Volume and Issue: 55, P. 101847 - 101847

Published: Jan. 1, 2023

Language: Английский

Citations

New indicator for a comprehensive evaluation of building energy performance through spatial and temporal dimensions DOI

Donglin Zhang, Yong Ding,

Lingxiao Fan

et al.

Energy and Buildings, Journal Year: 2023, Volume and Issue: 289, P. 113058 - 113058

Published: April 7, 2023

Language: Английский

Citations